# Function to compute divergence similarity
divergence <- function(behave_data){
divergence_df <- data.frame()
sample_max <- max(behave_data)
for (i in 1:length(behave_data)){
for (j in 1:length(behave_data)){
pair_vec <- c(behave_data[[i]], behave_data[[j]])
output_conv <- mean(pair_vec)
divergence_df[i, j] <- (sample_max - output_conv)
}}
return(divergence_df)
}
make_similarity_matrices <- function(behave_vec,
index,
center_value_list,
center_value_name_list,
resultpath,
save_out){
dataframes2loopthrough <- list()
behave_nn <- nearest_neighbours(behave_vec) # absolute difference
nn_name <- "behave_nn"
behave_conv <- convergence(behave_vec) # minimum pair
conv_name <- "behave_conv"
behave_div <- divergence(behave_vec) # max(sample) - minimum pair
div_name <- "behave_div"
df_names_finn <- c(nn_name, conv_name, div_name)
df_names_behave <- c()
df_names_punctuated <- c()
for (i in 1:length(center_value_list)){
behave_bow <- bow_tie(behave_vec, center_value_list[i]) # see function
behave_punct <- punctuated(behave_vec, center_value_list[i]) # see function
df_name <- paste0('punctuacted_', center_value_name_list[i])
df_names_behave[i] <- df_name
df_name_punct <- paste0('punctuated_nn_', center_value_name_list[i])
df_names_punctuated[i] <- df_name_punct
assign(df_name, behave_bow)
assign(df_name_punct, behave_punct)
}
df_names <- c(df_names_finn, df_names_behave, df_names_punctuated)
dataframes2loopthrough <- do.call("list", mget(df_names))
for (df in 1:length(dataframes2loopthrough)){
df2use <- dataframes2loopthrough[[df]]
dfname <- names(dataframes2loopthrough)
final_df <- scale_matrix(df2use)
# replace diagonal values with 1
diag(final_df) <- round(1, digits = 0)
rownames(final_df) <- index
assign(dfname[df], final_df)
saveoutfilepath <- file.path(resultpath,
paste0(dfname[df], ".csv"))
if(save_out == TRUE){
write.csv(final_df, saveoutfilepath, row.names=FALSE)}
}
final_dataframes2loopthrough <- do.call("list", mget(df_names))
return(final_dataframes2loopthrough)
} # end function run behave model similarity
# compute similarity on single value output
compute_single_variable_similarity <- function(single_v_data,
subids,
name2use,
resultpath,
save_out){
matrix_similarity = data.frame()
for (i in 1:length(single_v_data)){
for (j in 1:length(single_v_data)){
data_i = single_v_data[[i]]
data_j = single_v_data[[j]]
abs_diff = abs(data_i - data_j)
matrix_similarity[i, j] = abs_diff
}}
matrix_scaled = 1 - scale_matrix(matrix_similarity)
rownames(matrix_scaled) = subids
colnames(matrix_scaled) = subids
saveoutfilename <- paste0(name2use, ".csv")
saveoutfilepath <- file.path(resultpath,
saveoutfilename)
if (save_out == TRUE){
write.csv(matrix_scaled, saveoutfilepath, row.names=FALSE)}
return(as.matrix(matrix_scaled))
}
mantel_results_models <- function(x, y, nperm, n_models){
# function takes two separate lists of dataframes as arguments
# where x = list of data frames that model behavioral similarity (4 models)
# and y = list of data frames that model dependent variable similarity
# nperm is the n of permutations you want to run (i.e. 1000)
# n_models is the number of behavioral models you are using FIX ME
# using package vegan for the mantel() function, with spearman method*
# *because distribution of similarity is not parametric
# cit. https://jkzorz.github.io/2019/07/08/mantel-test.html
# NB: switched to the vegan package, because the metan one was defaulting into spearman and there was no way to edit that (we need a non-parametric test)
n_comparisons = length(y)
len_final_df = n_models*n_comparisons
model_res_r <- data.frame()
model_res_p <- data.frame()
rownames_df <- data.frame()
modelname = names(x)
comparisonname = names(y)
for (i in 1:length(x)){
for (j in 1:length(y)){
result_name <- paste0(modelname[i], comparisonname[j])
model2use = x[[i]]
data2use = y[[j]]
model_out = mantel(model2use,
data2use,
method = "spearman",
permutations = nperm,
na.rm = TRUE)
model_res_r <- rbind(model_res_r,
model_out$statistic) # r-value
model_res_p <- rbind(model_res_p,
model_out$signif) # p-value
rownames_df <- rbind(rownames_df, result_name)
}}
colnames(model_res_r) <- "r"
colnames(model_res_p) <- "p_value"
model_res <- cbind(model_res_p, model_res_r)
rownames(model_res) <- rownames_df[[1]]
model_res$p_value_adjusted <- p.adjust(model_res$p_value,
method = "fdr")
return(model_res)
} # end function mantel_results_models
compare_correlations <- function(r1_value,
r2_value,
alpha_level,
n_1,
n_2){
# NB: applies to BOTH Pearson or Spearman's
# REFs
# https://blogs.sas.com/content/iml/2017/09/20/fishers-transformation-correlation.html
# https://www.medcalc.org/manual/comparison-of-correlation-coefficients.php
# Fisher (1925): http://krishikosh.egranth.ac.in/bitstream/1/2048218/1/0039_2689A.pdf
# p_value from z score: https://www.r-bloggers.com/2022/05/calculate-the-p-value-from-z-score-in-r/
# as a test case I used the exact values in the Medcalc link
# and I got the same results
result_names <- c("r_values",
"z_scores",
"standard_error",
"Fisher's z",
"p_value",
"significance")
result <- vector("list", length(result_names))
names(result) <- result_names
# save the original r values
result[["r_values"]] <- c("r1" = r1_value, "r2" = r2_value)
# calculate z scores for the two pearson r coefficients
z1_value <- (1/2)*log((1+r1_value)/(1-r1_value))
z2_value <- (1/2)*log((1+r2_value)/(1-r2_value))
result[["z_scores"]] <- c("z1" = z1_value, "z2" = z2_value)
# calculate the standard error based on sample size
se_value1 <- (1/(n_1 - 3))
se_value2 <- (1/(n_2 - 3))
standard_error <- sqrt(se_value1 + se_value2)
result[["standard_error"]] <- standard_error
# compute Fisher's z
# NB: we need to take the absolute value because this
# eliminates the effect of which correlation is
# first and which is second
z <- abs((z1_value - z2_value)/standard_error)
result[["Fisher's z"]] <- z
# test significance of Fisher's z (rounded to 4 decimal places)
# need to see the lower.tail to FALSE as we are only
# evaluating positive z scores
p_value <- pnorm(z, lower.tail = FALSE)
p_value_formatted <- format(round(p_value, 4), nsmall = 4)
significant <- ifelse(p_value > alpha_level, "No", "Yes")
result[["p_value"]] <- p_value_formatted
result[["significance"]] <- significant
return(result)
}
library(easypackages)
libraries("here",
"ggplot2",
"tidyverse",
"psych",
"reticulate",
"graph4lg",
"ade4",
"vegan",
"similaritymodels",
"caret",
"tidyverse",
"robustbase",
"egg",
"reshape2",
"knitr",
"kableExtra",
"quantmod")
## Loading required package: here
## here() starts at /Users/scrockford/Library/CloudStorage/OneDrive-FondazioneIstitutoItalianoTecnologia/punctuated_similarity_proof
## Loading required package: ggplot2
## Loading required package: tidyverse
## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.4 ✔ readr 2.1.5
## ✔ forcats 1.0.0 ✔ stringr 1.5.1
## ✔ lubridate 1.9.4 ✔ tibble 3.2.1
## ✔ purrr 1.0.4 ✔ tidyr 1.3.1
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## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
## Loading required package: psych
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codepath = here("code")
resultpath = here("results")
datapath = here("data")
plotpath = here("plots")
# set random seed
set.seed(999)
# choose alpha (hypothesis testing sig. threshold)
alpha <- 0.05
# create list of tickers from US, UK, and German stock markets
# TICKER LIST AND RELATED COUNTRY
#
# FTSE: UK (FTSE 1000)
# GSPC: USA (S&P 500)
# GDAXI: Germany (GDAXI)
# FTSEMIB: Italy
# FCHI: France
# AXJO: Australia
# HSI: Hong Kong
# REF: https://www.sciencedirect.com/science/article/pii/S2405844024012337
ticker_list <- c("^FTSE", "^GSPC", "^GDAXI", "FTSEMIB.MI", "^FCHI", "^AXJO", "^HSI")
# Covid lockdown dates
#
# UK LEGAL COVID MANDATE (23/03/2020):
# https://www.instituteforgovernment.org.uk/sites/default/files/timeline-lockdown-web.pdf
# US BORDER CLOSER MANDATE (16/03/2020): https://www150.statcan.gc.ca/n1/pub/45-28-0001/2021001/article/00007-eng.htm
# GERMAN LEGAL COVID MANDATE (25/03/202):https://www.deutsche-apotheker-zeitung.de/news/artikel/2020/03/25/bundestag-stellt-epidemische-lage-von-nationaler-tragweite-fest
# ITALY COVID MANDATE (09/03/2020): https://en.wikipedia.org/wiki/COVID-19_lockdowns_in_Italy
# FRANCE COVID MANDATE (17/03/2020): https://www.france24.com/en/france/20210317-in-pictures-a-look-back-one-year-after-france-went-into-lockdown
# AUSTRALIA border closer (19/03/2020): https://www.timeout.com/melbourne/things-to-do/a-timeline-of-covid-19-in-australia-two-years-on
# WHO DECLARES PANDEMIC (11/03/2020): https://pmc.ncbi.nlm.nih.gov/articles/PMC7569573/
# CHINA declares SARS-CoV-2 sequence (12/01/2020): https://pmc.ncbi.nlm.nih.gov/articles/PMC7068164
# KEY dates to use:
#
# China declaration (early Jan 2020) (adding a day as the real date, 12th, is Sunday)
# WHO pandemic declaration (early March 2020)
# USA close borders (mid-march 2020)
lockdown_dates_strings <- c("2020-01-13", "2020-03-11", "2020-03-16")
center_value_name_list <- c("china", "who", "usa")
# n of age similarity models we will build
n_behave_models = 9
# number of permutations
nperm = 1000
# make plots pretty
#https://stackoverflow.com/questions/6736378/how-do-i-change-the-background-color-of-a-plot-made-with-ggplot2
mytheme <- list(
theme_classic()+
theme(panel.background = element_blank(),strip.background = element_rect(colour=NA, fill=NA),panel.border = element_rect(fill = NA, color = "black"),
legend.title = element_blank(),legend.position="bottom", strip.text = element_text(face="bold", size=9),
axis.text=element_text(face="bold"),axis.title = element_text(face="bold"),plot.title = element_text(face = "bold", hjust = 0.5,size=13))
)
# get data
# NB: the 1st of september 2019 was a sunday so starting on the 2nd
dates2use <- list()
indices2use <- list()
vals2use <- list()
for (ticker in ticker_list){
ticker_clean <- gsub("\\^", "", ticker)
df_name <- paste0(ticker_clean, "_df")
getSymbols(ticker, src="yahoo", from="2019-09-02", to ="2020-09-02")
df2use <- data.frame(get(ticker_clean))
df2use <- df2use[complete.cases(df2use), ]
assign(df_name, df2use)
# get change in time as distance in days from time 0 (02/09/2019)
dates_raw <- as.Date(gsub("-", "", rownames(df2use)), format = "%Y%m%d")
dates <- as.numeric(dates_raw - min(dates_raw))
# save dates to list
dates_name <- paste0(ticker_clean, "_dates")
dates2use[[dates_name]] <- dates
# get string dates as 'subject' ids (index column)
index2use <- rownames(df2use)
# save index to list
index_name <- paste0(ticker_clean, "_index")
indices2use[[index_name]] <- index2use
# get the corresponding indices for the pivotal dates we want to measure against
center_value <- c()
i = 1
for (date in lockdown_dates_strings){
get_date_index <- which(index2use == date)
center_value[i] <- dates[get_date_index]
i <- i + 1
}
# store out the corresponding times (n days from time point 0)
center_name <- paste0(ticker_clean, "_center")
vals2use[[center_name]] <- center_value
# check lengths of dfs (unless missing data should be of mostly equal length)
print(paste0("Number of dates in ", ticker_clean, ": ", nrow(df2use)))
}
## [1] "Number of dates in FTSE: 254"
## [1] "Number of dates in GSPC: 253"
## [1] "Number of dates in GDAXI: 252"
## [1] "Number of dates in FTSEMIB.MI: 253"
## [1] "Number of dates in FCHI: 256"
## [1] "Number of dates in AXJO: 255"
## [1] "Number of dates in HSI: 249"
# NOT IN A LOOP SO I CAN SEE TICKER ON GRAPHS
# REF: https://stat-wizards.github.io/Forcasting-A-Time-Series-Stock-Market-Data/
print(chartSeries(FTSE, TA = NULL))
## An object of class "chob"
## Slot "device":
## [1] 2
##
## Slot "call":
## chartSeries(x = FTSE, TA = NULL)
##
## Slot "xdata":
## FTSE.Open FTSE.High FTSE.Low FTSE.Close FTSE.Volume FTSE.Adjusted
## 2019-09-02 7207.2 7315.3 7206.9 7281.9 497764900 7281.9
## 2019-09-03 7281.9 7301.5 7239.1 7268.2 626095300 7268.2
## 2019-09-04 7268.2 7334.6 7268.2 7311.3 677444300 7311.3
## 2019-09-05 7311.3 7330.7 7250.6 7271.2 711302800 7271.2
## 2019-09-06 7271.2 7284.1 7244.1 7282.3 692287900 7282.3
## 2019-09-09 7282.3 7325.2 7206.0 7235.8 794831900 7235.8
## 2019-09-10 7235.8 7270.5 7199.4 7268.0 1031549500 7268.0
## 2019-09-11 7268.0 7346.7 7268.0 7338.0 829121400 7338.0
## 2019-09-12 7338.0 7369.3 7303.2 7344.7 725259800 7344.7
## 2019-09-13 7344.7 7380.3 7318.2 7367.5 1003827600 7367.5
## ...
## 2020-08-18 6127.4 6162.7 6062.6 6076.6 502242600 6076.6
## 2020-08-19 6076.6 6114.8 6045.0 6112.0 425040900 6112.0
## 2020-08-20 6112.0 6112.0 6009.6 6013.3 505170900 6013.3
## 2020-08-21 6013.3 6036.5 5948.8 6001.9 545211700 6001.9
## 2020-08-24 6001.9 6119.8 6001.9 6104.7 452722200 6104.7
## 2020-08-25 6104.7 6173.5 6032.1 6037.0 538992400 6037.0
## 2020-08-26 6037.0 6050.8 5992.2 6045.6 405657200 6045.6
## 2020-08-27 6045.6 6062.5 6000.0 6000.0 496427000 6000.0
## 2020-08-28 6000.0 6033.0 5962.5 5963.6 928308900 5963.6
## 2020-09-01 5963.6 5972.5 5824.0 5862.1 1041226100 5862.1
##
## Slot "xsubset":
## [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18
## [19] 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36
## [37] 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54
## [55] 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72
## [73] 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90
## [91] 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108
## [109] 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126
## [127] 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144
## [145] 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162
## [163] 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180
## [181] 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198
## [199] 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216
## [217] 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234
## [235] 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252
## [253] 253 254
##
## Slot "name":
## [1] "FTSE"
##
## Slot "type":
## [1] "candlesticks"
##
## Slot "passed.args":
## $x
## FTSE
##
## $TA
## list()
##
##
## Slot "windows":
## [1] 1
##
## Slot "xrange":
## [1] 1 254
##
## Slot "yrange":
## [1] 4898.8 7689.7
##
## Slot "log.scale":
## [1] FALSE
##
## Slot "length":
## [1] 254
##
## Slot "color.vol":
## [1] TRUE
##
## Slot "multi.col":
## [1] FALSE
##
## Slot "show.vol":
## [1] TRUE
##
## Slot "show.grid":
## [1] TRUE
##
## Slot "line.type":
## [1] "l"
##
## Slot "bar.type":
## [1] "ohlc"
##
## Slot "xlab":
## character(0)
##
## Slot "ylab":
## character(0)
##
## Slot "spacing":
## [1] 3
##
## Slot "width":
## [1] 3
##
## Slot "bp":
## Sep 02\n2019 Oct 01\n2019 Nov 01\n2019 Dec 02\n2019 Jan 02\n2020 Feb 03\n2020
## 1 22 45 66 86 108
## Mar 02\n2020 Apr 01\n2020 May 01\n2020 Jun 01\n2020 Jul 01\n2020 Aug 03\n2020
## 128 150 170 189 211 234
## Sep 01\n2020 Sep 01\n2020
## 254 254
##
## Slot "x.labels":
## [1] "Sep 02\n2019" "Oct 01\n2019" "Nov 01\n2019" "Dec 02\n2019" "Jan 02\n2020"
## [6] "Feb 03\n2020" "Mar 02\n2020" "Apr 01\n2020" "May 01\n2020" "Jun 01\n2020"
## [11] "Jul 01\n2020" "Aug 03\n2020" "Sep 01\n2020" "Sep 01\n2020"
##
## Slot "colors":
## List of 27
## $ fg.col : chr "#666666"
## $ bg.col : chr "#222222"
## $ grid.col : chr "#303030"
## $ border : chr "#666666"
## $ minor.tick : chr "#303030"
## $ major.tick : chr "#AAAAAA"
## $ up.col : chr "#00FF00"
## $ dn.col : chr "#FF9900"
## $ dn.up.col : chr "#00FF00"
## $ up.up.col : chr "#00FF00"
## $ dn.dn.col : chr "#FF9900"
## $ up.dn.col : chr "#FF9900"
## $ up.border : chr "#666666"
## $ dn.border : chr "#666666"
## $ dn.up.border: chr "#666666"
## $ up.up.border: chr "#666666"
## $ dn.dn.border: chr "#666666"
## $ up.dn.border: chr "#666666"
## $ main.col : chr "#999999"
## $ sub.col : chr "#999999"
## $ area : chr "#252525"
## $ fill : chr "#282828"
## $ Expiry : chr "#383838"
## $ BBands.col : chr "red"
## $ BBands.fill : chr "#282828"
## $ BBands :List of 2
## ..$ col : chr "red"
## ..$ fill: chr "#282828"
## $ theme.name : chr "black"
## - attr(*, "class")= chr "chart.theme"
##
## Slot "layout":
## [1] NA
##
## Slot "time.scale":
## [1] "daily"
##
## Slot "minor.ticks":
## [1] TRUE
##
## Slot "major.ticks":
## [1] "auto"
print(chartSeries(GSPC, TA = NULL))
## An object of class "chob"
## Slot "device":
## [1] 2
##
## Slot "call":
## chartSeries(x = GSPC, TA = NULL)
##
## Slot "xdata":
## GSPC.Open GSPC.High GSPC.Low GSPC.Close GSPC.Volume GSPC.Adjusted
## 2019-09-03 2909.01 2914.39 2891.85 2906.27 3427830000 2906.27
## 2019-09-04 2924.67 2938.84 2921.86 2937.78 3167900000 2937.78
## 2019-09-05 2960.60 2985.86 2960.60 2976.00 3902600000 2976.00
## 2019-09-06 2980.33 2985.03 2972.51 2978.71 3209340000 2978.71
## 2019-09-09 2988.43 2989.43 2969.39 2978.43 4031120000 2978.43
## 2019-09-10 2971.01 2979.39 2957.01 2979.39 4393040000 2979.39
## 2019-09-11 2981.41 3000.93 2975.31 3000.93 3934370000 3000.93
## 2019-09-12 3009.08 3020.74 3000.92 3009.57 3796990000 3009.57
## 2019-09-13 3012.21 3017.33 3002.90 3007.39 3557010000 3007.39
## 2019-09-16 2996.41 3002.19 2990.67 2997.96 4285860000 2997.96
## ...
## 2020-08-19 3392.51 3399.54 3369.66 3374.85 3679480000 3374.85
## 2020-08-20 3360.48 3390.80 3354.69 3385.51 3431040000 3385.51
## 2020-08-21 3386.01 3399.96 3379.31 3397.16 3505010000 3397.16
## 2020-08-24 3418.09 3432.09 3413.13 3431.28 3743410000 3431.28
## 2020-08-25 3435.95 3444.21 3425.84 3443.62 3627650000 3443.62
## 2020-08-26 3449.97 3481.07 3444.15 3478.73 3780530000 3478.73
## 2020-08-27 3485.14 3501.38 3468.35 3484.55 3955890000 3484.55
## 2020-08-28 3494.69 3509.23 3484.32 3508.01 3868510000 3508.01
## 2020-08-31 3509.73 3514.77 3493.25 3500.31 4348280000 3500.31
## 2020-09-01 3507.44 3528.03 3494.60 3526.65 4101490000 3526.65
##
## Slot "xsubset":
## [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18
## [19] 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36
## [37] 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54
## [55] 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72
## [73] 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90
## [91] 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108
## [109] 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126
## [127] 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144
## [145] 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162
## [163] 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180
## [181] 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198
## [199] 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216
## [217] 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234
## [235] 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252
## [253] 253
##
## Slot "name":
## [1] "GSPC"
##
## Slot "type":
## [1] "candlesticks"
##
## Slot "passed.args":
## $x
## GSPC
##
## $TA
## list()
##
##
## Slot "windows":
## [1] 1
##
## Slot "xrange":
## [1] 1 253
##
## Slot "yrange":
## [1] 2191.86 3528.03
##
## Slot "log.scale":
## [1] FALSE
##
## Slot "length":
## [1] 253
##
## Slot "color.vol":
## [1] TRUE
##
## Slot "multi.col":
## [1] FALSE
##
## Slot "show.vol":
## [1] TRUE
##
## Slot "show.grid":
## [1] TRUE
##
## Slot "line.type":
## [1] "l"
##
## Slot "bar.type":
## [1] "ohlc"
##
## Slot "xlab":
## character(0)
##
## Slot "ylab":
## character(0)
##
## Slot "spacing":
## [1] 3
##
## Slot "width":
## [1] 3
##
## Slot "bp":
## Sep 03\n2019 Oct 01\n2019 Nov 01\n2019 Dec 02\n2019 Jan 02\n2020 Feb 03\n2020
## 1 21 44 64 85 106
## Mar 02\n2020 Apr 01\n2020 May 01\n2020 Jun 01\n2020 Jul 01\n2020 Aug 03\n2020
## 125 147 168 188 210 232
## Sep 01\n2020 Sep 01\n2020
## 253 253
##
## Slot "x.labels":
## [1] "Sep 03\n2019" "Oct 01\n2019" "Nov 01\n2019" "Dec 02\n2019" "Jan 02\n2020"
## [6] "Feb 03\n2020" "Mar 02\n2020" "Apr 01\n2020" "May 01\n2020" "Jun 01\n2020"
## [11] "Jul 01\n2020" "Aug 03\n2020" "Sep 01\n2020" "Sep 01\n2020"
##
## Slot "colors":
## List of 27
## $ fg.col : chr "#666666"
## $ bg.col : chr "#222222"
## $ grid.col : chr "#303030"
## $ border : chr "#666666"
## $ minor.tick : chr "#303030"
## $ major.tick : chr "#AAAAAA"
## $ up.col : chr "#00FF00"
## $ dn.col : chr "#FF9900"
## $ dn.up.col : chr "#00FF00"
## $ up.up.col : chr "#00FF00"
## $ dn.dn.col : chr "#FF9900"
## $ up.dn.col : chr "#FF9900"
## $ up.border : chr "#666666"
## $ dn.border : chr "#666666"
## $ dn.up.border: chr "#666666"
## $ up.up.border: chr "#666666"
## $ dn.dn.border: chr "#666666"
## $ up.dn.border: chr "#666666"
## $ main.col : chr "#999999"
## $ sub.col : chr "#999999"
## $ area : chr "#252525"
## $ fill : chr "#282828"
## $ Expiry : chr "#383838"
## $ BBands.col : chr "red"
## $ BBands.fill : chr "#282828"
## $ BBands :List of 2
## ..$ col : chr "red"
## ..$ fill: chr "#282828"
## $ theme.name : chr "black"
## - attr(*, "class")= chr "chart.theme"
##
## Slot "layout":
## [1] NA
##
## Slot "time.scale":
## [1] "daily"
##
## Slot "minor.ticks":
## [1] TRUE
##
## Slot "major.ticks":
## [1] "auto"
print(chartSeries(GDAXI, TA = NULL))
## An object of class "chob"
## Slot "device":
## [1] 2
##
## Slot "call":
## chartSeries(x = GDAXI, TA = NULL)
##
## Slot "xdata":
## GDAXI.Open GDAXI.High GDAXI.Low GDAXI.Close GDAXI.Volume
## 2019-09-02 11939.99 11994.11 11929.91 11953.78 46207600
## 2019-09-03 11921.94 11956.69 11869.28 11910.86 66704200
## 2019-09-04 12043.96 12078.40 11999.83 12025.04 63323800
## 2019-09-05 12117.90 12151.31 12084.17 12126.78 89831600
## 2019-09-06 12146.00 12205.10 12131.29 12191.73 80411000
## 2019-09-09 12210.87 12245.11 12189.60 12226.10 74246100
## 2019-09-10 12210.88 12292.14 12179.88 12268.71 107899800
## 2019-09-11 12341.84 12394.28 12317.61 12359.07 90579200
## 2019-09-12 12399.40 12471.83 12311.81 12410.25 111214300
## 2019-09-13 12412.72 12494.25 12408.93 12468.53 90990500
## ...
## 2020-08-19 12838.63 12980.70 12833.80 12977.33 52633400
## 2020-08-20 12829.39 12891.14 12755.52 12830.00 54738500
## 2020-08-21 12879.45 12911.27 12633.71 12764.80 71999900
## 2020-08-24 12945.97 13104.31 12924.70 13066.54 64175000
## 2020-08-25 13136.77 13221.82 13060.87 13061.62 55974200
## 2020-08-26 13041.83 13192.32 13010.53 13190.15 46701500
## 2020-08-27 13206.58 13218.05 13087.37 13096.36 53031400
## 2020-08-28 13140.60 13147.24 12951.26 13033.20 63768900
## 2020-08-31 13103.93 13148.19 12923.76 12945.38 59561900
## 2020-09-01 13037.20 13127.28 12850.30 12974.25 63053900
## GDAXI.Adjusted
## 2019-09-02 11953.78
## 2019-09-03 11910.86
## 2019-09-04 12025.04
## 2019-09-05 12126.78
## 2019-09-06 12191.73
## 2019-09-09 12226.10
## 2019-09-10 12268.71
## 2019-09-11 12359.07
## 2019-09-12 12410.25
## 2019-09-13 12468.53
## ...
## 2020-08-19 12977.33
## 2020-08-20 12830.00
## 2020-08-21 12764.80
## 2020-08-24 13066.54
## 2020-08-25 13061.62
## 2020-08-26 13190.15
## 2020-08-27 13096.36
## 2020-08-28 13033.20
## 2020-08-31 12945.38
## 2020-09-01 12974.25
##
## Slot "xsubset":
## [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18
## [19] 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36
## [37] 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54
## [55] 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72
## [73] 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90
## [91] 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108
## [109] 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126
## [127] 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144
## [145] 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162
## [163] 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180
## [181] 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198
## [199] 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216
## [217] 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234
## [235] 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252
##
## Slot "name":
## [1] "GDAXI"
##
## Slot "type":
## [1] "candlesticks"
##
## Slot "passed.args":
## $x
## GDAXI
##
## $TA
## list()
##
##
## Slot "windows":
## [1] 1
##
## Slot "xrange":
## [1] 1 252
##
## Slot "yrange":
## [1] 8255.65 13795.24
##
## Slot "log.scale":
## [1] FALSE
##
## Slot "length":
## [1] 252
##
## Slot "color.vol":
## [1] TRUE
##
## Slot "multi.col":
## [1] FALSE
##
## Slot "show.vol":
## [1] TRUE
##
## Slot "show.grid":
## [1] TRUE
##
## Slot "line.type":
## [1] "l"
##
## Slot "bar.type":
## [1] "ohlc"
##
## Slot "xlab":
## character(0)
##
## Slot "ylab":
## character(0)
##
## Slot "spacing":
## [1] 3
##
## Slot "width":
## [1] 3
##
## Slot "bp":
## Sep 02\n2019 Oct 01\n2019 Nov 01\n2019 Dec 02\n2019 Jan 02\n2020 Feb 03\n2020
## 1 22 44 65 83 105
## Mar 02\n2020 Apr 01\n2020 May 04\n2020 Jun 02\n2020 Jul 01\n2020 Aug 03\n2020
## 125 147 167 187 208 231
## Sep 01\n2020 Sep 01\n2020
## 252 252
##
## Slot "x.labels":
## [1] "Sep 02\n2019" "Oct 01\n2019" "Nov 01\n2019" "Dec 02\n2019" "Jan 02\n2020"
## [6] "Feb 03\n2020" "Mar 02\n2020" "Apr 01\n2020" "May 04\n2020" "Jun 02\n2020"
## [11] "Jul 01\n2020" "Aug 03\n2020" "Sep 01\n2020" "Sep 01\n2020"
##
## Slot "colors":
## List of 27
## $ fg.col : chr "#666666"
## $ bg.col : chr "#222222"
## $ grid.col : chr "#303030"
## $ border : chr "#666666"
## $ minor.tick : chr "#303030"
## $ major.tick : chr "#AAAAAA"
## $ up.col : chr "#00FF00"
## $ dn.col : chr "#FF9900"
## $ dn.up.col : chr "#00FF00"
## $ up.up.col : chr "#00FF00"
## $ dn.dn.col : chr "#FF9900"
## $ up.dn.col : chr "#FF9900"
## $ up.border : chr "#666666"
## $ dn.border : chr "#666666"
## $ dn.up.border: chr "#666666"
## $ up.up.border: chr "#666666"
## $ dn.dn.border: chr "#666666"
## $ up.dn.border: chr "#666666"
## $ main.col : chr "#999999"
## $ sub.col : chr "#999999"
## $ area : chr "#252525"
## $ fill : chr "#282828"
## $ Expiry : chr "#383838"
## $ BBands.col : chr "red"
## $ BBands.fill : chr "#282828"
## $ BBands :List of 2
## ..$ col : chr "red"
## ..$ fill: chr "#282828"
## $ theme.name : chr "black"
## - attr(*, "class")= chr "chart.theme"
##
## Slot "layout":
## [1] NA
##
## Slot "time.scale":
## [1] "daily"
##
## Slot "minor.ticks":
## [1] TRUE
##
## Slot "major.ticks":
## [1] "auto"
print(chartSeries(FTSEMIB.MI, TA = NULL))
## An object of class "chob"
## Slot "device":
## [1] 2
##
## Slot "call":
## chartSeries(x = FTSEMIB.MI, TA = NULL)
##
## Slot "xdata":
## FTSEMIB.MI.Open FTSEMIB.MI.High FTSEMIB.MI.Low FTSEMIB.MI.Close
## 2019-09-02 21390 21567 21389 21452
## 2019-09-03 21438 21497 21311 21399
## 2019-09-04 21665 21777 21630 21738
## 2019-09-05 21853 21955 21796 21955
## 2019-09-06 21967 22010 21857 21947
## 2019-09-09 22008 22041 21935 21990
## 2019-09-10 21964 21989 21824 21869
## 2019-09-11 21994 22033 21796 21892
## 2019-09-12 21983 22141 21835 22083
## 2019-09-13 22127 22230 22076 22181
## ...
## 2020-08-19 19833 20055 19734 20055
## 2020-08-20 19827 19921 19719 19767
## 2020-08-21 19846 19868 19480 19695
## 2020-08-24 19908 20130 19887 20113
## 2020-08-25 20256 20368 20030 20030
## 2020-08-26 19971 20137 19928 20137
## 2020-08-27 20131 20133 19847 19847
## 2020-08-28 19953 19992 19687 19841
## 2020-08-31 19972 20081 19616 19634
## 2020-09-01 19776 19871 19435 19595
## FTSEMIB.MI.Volume FTSEMIB.MI.Adjusted
## 2019-09-02 227807500 21452
## 2019-09-03 283939100 21399
## 2019-09-04 408310000 21738
## 2019-09-05 444458900 21955
## 2019-09-06 396569400 21947
## 2019-09-09 358176500 21990
## 2019-09-10 465833900 21869
## 2019-09-11 520578100 21892
## 2019-09-12 576922200 22083
## 2019-09-13 489149200 22181
## ...
## 2020-08-19 221316800 20055
## 2020-08-20 264922400 19767
## 2020-08-21 300533200 19695
## 2020-08-24 304828800 20113
## 2020-08-25 327848800 20030
## 2020-08-26 336048100 20137
## 2020-08-27 479171100 19847
## 2020-08-28 475736200 19841
## 2020-08-31 411640300 19634
## 2020-09-01 429280800 19595
##
## Slot "xsubset":
## [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18
## [19] 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36
## [37] 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54
## [55] 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72
## [73] 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90
## [91] 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108
## [109] 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126
## [127] 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144
## [145] 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162
## [163] 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180
## [181] 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198
## [199] 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216
## [217] 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234
## [235] 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252
## [253] 253
##
## Slot "name":
## [1] "FTSEMIB.MI"
##
## Slot "type":
## [1] "candlesticks"
##
## Slot "passed.args":
## $x
## FTSEMIB.MI
##
## $TA
## list()
##
##
## Slot "windows":
## [1] 1
##
## Slot "xrange":
## [1] 1 253
##
## Slot "yrange":
## [1] 14153 25483
##
## Slot "log.scale":
## [1] FALSE
##
## Slot "length":
## [1] 253
##
## Slot "color.vol":
## [1] TRUE
##
## Slot "multi.col":
## [1] FALSE
##
## Slot "show.vol":
## [1] TRUE
##
## Slot "show.grid":
## [1] TRUE
##
## Slot "line.type":
## [1] "l"
##
## Slot "bar.type":
## [1] "ohlc"
##
## Slot "xlab":
## character(0)
##
## Slot "ylab":
## character(0)
##
## Slot "spacing":
## [1] 3
##
## Slot "width":
## [1] 3
##
## Slot "bp":
## Sep 02\n2019 Oct 01\n2019 Nov 01\n2019 Dec 02\n2019 Jan 02\n2020 Feb 03\n2020
## 1 21 44 65 83 105
## Mar 02\n2020 Apr 01\n2020 May 04\n2020 Jun 01\n2020 Jul 01\n2020 Aug 03\n2020
## 125 147 167 187 209 232
## Sep 01\n2020 Sep 01\n2020
## 253 253
##
## Slot "x.labels":
## [1] "Sep 02\n2019" "Oct 01\n2019" "Nov 01\n2019" "Dec 02\n2019" "Jan 02\n2020"
## [6] "Feb 03\n2020" "Mar 02\n2020" "Apr 01\n2020" "May 04\n2020" "Jun 01\n2020"
## [11] "Jul 01\n2020" "Aug 03\n2020" "Sep 01\n2020" "Sep 01\n2020"
##
## Slot "colors":
## List of 27
## $ fg.col : chr "#666666"
## $ bg.col : chr "#222222"
## $ grid.col : chr "#303030"
## $ border : chr "#666666"
## $ minor.tick : chr "#303030"
## $ major.tick : chr "#AAAAAA"
## $ up.col : chr "#00FF00"
## $ dn.col : chr "#FF9900"
## $ dn.up.col : chr "#00FF00"
## $ up.up.col : chr "#00FF00"
## $ dn.dn.col : chr "#FF9900"
## $ up.dn.col : chr "#FF9900"
## $ up.border : chr "#666666"
## $ dn.border : chr "#666666"
## $ dn.up.border: chr "#666666"
## $ up.up.border: chr "#666666"
## $ dn.dn.border: chr "#666666"
## $ up.dn.border: chr "#666666"
## $ main.col : chr "#999999"
## $ sub.col : chr "#999999"
## $ area : chr "#252525"
## $ fill : chr "#282828"
## $ Expiry : chr "#383838"
## $ BBands.col : chr "red"
## $ BBands.fill : chr "#282828"
## $ BBands :List of 2
## ..$ col : chr "red"
## ..$ fill: chr "#282828"
## $ theme.name : chr "black"
## - attr(*, "class")= chr "chart.theme"
##
## Slot "layout":
## [1] NA
##
## Slot "time.scale":
## [1] "daily"
##
## Slot "minor.ticks":
## [1] TRUE
##
## Slot "major.ticks":
## [1] "auto"
print(chartSeries(FCHI, TA = NULL))
## An object of class "chob"
## Slot "device":
## [1] 2
##
## Slot "call":
## chartSeries(x = FCHI, TA = NULL)
##
## Slot "xdata":
## FCHI.Open FCHI.High FCHI.Low FCHI.Close FCHI.Volume FCHI.Adjusted
## 2019-09-02 5483.43 5502.58 5479.82 5493.04 42347400 5493.04
## 2019-09-03 5484.36 5484.54 5441.18 5466.07 57453400 5466.07
## 2019-09-04 5518.92 5537.10 5508.50 5532.07 60939800 5532.07
## 2019-09-05 5569.59 5605.88 5559.82 5593.37 84379600 5593.37
## 2019-09-06 5592.07 5610.70 5581.54 5603.99 71138100 5603.99
## 2019-09-09 5606.36 5611.59 5579.93 5588.95 73880200 5588.95
## 2019-09-10 5586.88 5596.94 5555.51 5593.21 106338200 5593.21
## 2019-09-11 5606.43 5626.05 5606.43 5618.06 92098300 5618.06
## 2019-09-12 5633.95 5667.46 5596.37 5642.86 110360200 5642.86
## 2019-09-13 5649.23 5672.07 5638.17 5655.46 85595400 5655.46
## ...
## 2020-08-19 4934.79 4977.23 4917.53 4977.23 53103600 4977.23
## 2020-08-20 4914.69 4937.95 4888.15 4911.24 66410900 4911.24
## 2020-08-21 4927.61 4939.25 4839.08 4896.33 72886800 4896.33
## 2020-08-24 4948.71 5013.70 4948.71 5007.89 68837200 5007.89
## 2020-08-25 5023.06 5073.61 5008.27 5008.27 64060000 5008.27
## 2020-08-26 4991.63 5050.10 4977.94 5048.43 49317700 5048.43
## 2020-08-27 5052.26 5052.26 5005.46 5015.97 67454700 5015.97
## 2020-08-28 5031.31 5031.53 4970.64 5002.94 71208000 5002.94
## 2020-08-31 5041.34 5067.55 4942.18 4947.22 97432500 4947.22
## 2020-09-01 4974.42 4993.56 4892.83 4938.10 91322100 4938.10
##
## Slot "xsubset":
## [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18
## [19] 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36
## [37] 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54
## [55] 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72
## [73] 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90
## [91] 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108
## [109] 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126
## [127] 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144
## [145] 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162
## [163] 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180
## [181] 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198
## [199] 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216
## [217] 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234
## [235] 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252
## [253] 253 254 255 256
##
## Slot "name":
## [1] "FCHI"
##
## Slot "type":
## [1] "candlesticks"
##
## Slot "passed.args":
## $x
## FCHI
##
## $TA
## list()
##
##
## Slot "windows":
## [1] 1
##
## Slot "xrange":
## [1] 1 256
##
## Slot "yrange":
## [1] 3632.06 6111.41
##
## Slot "log.scale":
## [1] FALSE
##
## Slot "length":
## [1] 256
##
## Slot "color.vol":
## [1] TRUE
##
## Slot "multi.col":
## [1] FALSE
##
## Slot "show.vol":
## [1] TRUE
##
## Slot "show.grid":
## [1] TRUE
##
## Slot "line.type":
## [1] "l"
##
## Slot "bar.type":
## [1] "ohlc"
##
## Slot "xlab":
## character(0)
##
## Slot "ylab":
## character(0)
##
## Slot "spacing":
## [1] 3
##
## Slot "width":
## [1] 3
##
## Slot "bp":
## Sep 02\n2019 Oct 01\n2019 Nov 01\n2019 Dec 02\n2019 Jan 02\n2020 Feb 03\n2020
## 1 22 45 66 86 108
## Mar 02\n2020 Apr 01\n2020 May 04\n2020 Jun 01\n2020 Jul 01\n2020 Aug 03\n2020
## 128 150 170 190 212 235
## Sep 01\n2020 Sep 01\n2020
## 256 256
##
## Slot "x.labels":
## [1] "Sep 02\n2019" "Oct 01\n2019" "Nov 01\n2019" "Dec 02\n2019" "Jan 02\n2020"
## [6] "Feb 03\n2020" "Mar 02\n2020" "Apr 01\n2020" "May 04\n2020" "Jun 01\n2020"
## [11] "Jul 01\n2020" "Aug 03\n2020" "Sep 01\n2020" "Sep 01\n2020"
##
## Slot "colors":
## List of 27
## $ fg.col : chr "#666666"
## $ bg.col : chr "#222222"
## $ grid.col : chr "#303030"
## $ border : chr "#666666"
## $ minor.tick : chr "#303030"
## $ major.tick : chr "#AAAAAA"
## $ up.col : chr "#00FF00"
## $ dn.col : chr "#FF9900"
## $ dn.up.col : chr "#00FF00"
## $ up.up.col : chr "#00FF00"
## $ dn.dn.col : chr "#FF9900"
## $ up.dn.col : chr "#FF9900"
## $ up.border : chr "#666666"
## $ dn.border : chr "#666666"
## $ dn.up.border: chr "#666666"
## $ up.up.border: chr "#666666"
## $ dn.dn.border: chr "#666666"
## $ up.dn.border: chr "#666666"
## $ main.col : chr "#999999"
## $ sub.col : chr "#999999"
## $ area : chr "#252525"
## $ fill : chr "#282828"
## $ Expiry : chr "#383838"
## $ BBands.col : chr "red"
## $ BBands.fill : chr "#282828"
## $ BBands :List of 2
## ..$ col : chr "red"
## ..$ fill: chr "#282828"
## $ theme.name : chr "black"
## - attr(*, "class")= chr "chart.theme"
##
## Slot "layout":
## [1] NA
##
## Slot "time.scale":
## [1] "daily"
##
## Slot "minor.ticks":
## [1] TRUE
##
## Slot "major.ticks":
## [1] "auto"
print(chartSeries(AXJO, TA = NULL))
## An object of class "chob"
## Slot "device":
## [1] 2
##
## Slot "call":
## chartSeries(x = AXJO, TA = NULL)
##
## Slot "xdata":
## AXJO.Open AXJO.High AXJO.Low AXJO.Close AXJO.Volume AXJO.Adjusted
## 2019-09-02 6604.2 6604.8 6558.3 6579.4 525000 6579.4
## 2019-09-03 6579.4 6591.3 6554.3 6573.4 479400 6573.4
## 2019-09-04 6573.4 6573.4 6503.9 6553.0 695600 6553.0
## 2019-09-05 6553.0 6622.8 6551.0 6613.2 796300 6613.2
## 2019-09-06 6613.2 6656.1 6613.2 6647.3 702600 6647.3
## 2019-09-09 6647.3 6659.7 6631.2 6648.0 542900 6648.0
## 2019-09-10 6648.0 6654.8 6595.7 6614.1 746300 6614.1
## 2019-09-11 6614.1 6638.4 6614.1 6638.0 811400 6638.0
## 2019-09-12 6638.0 6687.4 6638.0 6654.9 662600 6654.9
## 2019-09-13 6654.9 6676.5 6653.7 6669.2 673900 6669.2
## ...
## 2020-08-20 6167.6 6167.6 6096.7 6120.0 867100 6120.0
## 2020-08-21 6120.0 6166.4 6104.8 6111.2 812200 6111.2
## 2020-08-24 6111.2 6134.8 6094.7 6129.6 793800 6129.6
## 2020-08-25 6129.6 6199.2 6129.6 6161.4 901000 6161.4
## 2020-08-26 6160.4 6160.4 6079.8 6116.4 886400 6116.4
## 2020-08-27 6119.1 6159.8 6117.1 6126.2 824900 6126.2
## 2020-08-28 6126.2 6126.2 6056.0 6073.8 785300 6073.8
## 2020-08-31 6073.8 6093.9 6060.5 6060.5 811400 6060.5
## 2020-09-01 6060.5 6060.5 5908.9 5953.4 885100 5953.4
## 2020-09-02 5953.4 6075.7 5953.4 6063.2 786400 6063.2
##
## Slot "xsubset":
## [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18
## [19] 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36
## [37] 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54
## [55] 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72
## [73] 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90
## [91] 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108
## [109] 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126
## [127] 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144
## [145] 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162
## [163] 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180
## [181] 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198
## [199] 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216
## [217] 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234
## [235] 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252
## [253] 253 254 255
##
## Slot "name":
## [1] "AXJO"
##
## Slot "type":
## [1] "candlesticks"
##
## Slot "passed.args":
## $x
## AXJO
##
## $TA
## list()
##
##
## Slot "windows":
## [1] 1
##
## Slot "xrange":
## [1] 1 255
##
## Slot "yrange":
## [1] 4402.5 7197.2
##
## Slot "log.scale":
## [1] FALSE
##
## Slot "length":
## [1] 255
##
## Slot "color.vol":
## [1] TRUE
##
## Slot "multi.col":
## [1] FALSE
##
## Slot "show.vol":
## [1] TRUE
##
## Slot "show.grid":
## [1] TRUE
##
## Slot "line.type":
## [1] "l"
##
## Slot "bar.type":
## [1] "ohlc"
##
## Slot "xlab":
## character(0)
##
## Slot "ylab":
## character(0)
##
## Slot "spacing":
## [1] 3
##
## Slot "width":
## [1] 3
##
## Slot "bp":
## Sep 02\n2019 Oct 01\n2019 Nov 03\n2019 Dec 01\n2019 Jan 01\n2020 Feb 02\n2020
## 1 22 45 65 85 106
## Mar 01\n2020 Apr 01\n2020 May 01\n2020 Jun 01\n2020 Jul 01\n2020 Aug 03\n2020
## 126 149 168 189 210 233
## Sep 01\n2020 Sep 02\n2020
## 254 255
##
## Slot "x.labels":
## [1] "Sep 02\n2019" "Oct 01\n2019" "Nov 03\n2019" "Dec 01\n2019" "Jan 01\n2020"
## [6] "Feb 02\n2020" "Mar 01\n2020" "Apr 01\n2020" "May 01\n2020" "Jun 01\n2020"
## [11] "Jul 01\n2020" "Aug 03\n2020" "Sep 01\n2020" "Sep 02\n2020"
##
## Slot "colors":
## List of 27
## $ fg.col : chr "#666666"
## $ bg.col : chr "#222222"
## $ grid.col : chr "#303030"
## $ border : chr "#666666"
## $ minor.tick : chr "#303030"
## $ major.tick : chr "#AAAAAA"
## $ up.col : chr "#00FF00"
## $ dn.col : chr "#FF9900"
## $ dn.up.col : chr "#00FF00"
## $ up.up.col : chr "#00FF00"
## $ dn.dn.col : chr "#FF9900"
## $ up.dn.col : chr "#FF9900"
## $ up.border : chr "#666666"
## $ dn.border : chr "#666666"
## $ dn.up.border: chr "#666666"
## $ up.up.border: chr "#666666"
## $ dn.dn.border: chr "#666666"
## $ up.dn.border: chr "#666666"
## $ main.col : chr "#999999"
## $ sub.col : chr "#999999"
## $ area : chr "#252525"
## $ fill : chr "#282828"
## $ Expiry : chr "#383838"
## $ BBands.col : chr "red"
## $ BBands.fill : chr "#282828"
## $ BBands :List of 2
## ..$ col : chr "red"
## ..$ fill: chr "#282828"
## $ theme.name : chr "black"
## - attr(*, "class")= chr "chart.theme"
##
## Slot "layout":
## [1] NA
##
## Slot "time.scale":
## [1] "daily"
##
## Slot "minor.ticks":
## [1] TRUE
##
## Slot "major.ticks":
## [1] "auto"
print(chartSeries(HSI, TA = NULL))
## An object of class "chob"
## Slot "device":
## [1] 2
##
## Slot "call":
## chartSeries(x = HSI, TA = NULL)
##
## Slot "xdata":
## HSI.Open HSI.High HSI.Low HSI.Close HSI.Volume HSI.Adjusted
## 2019-09-02 25627.83 25662.31 25502.70 25626.55 1295066700 25626.55
## 2019-09-03 25546.32 25736.05 25498.11 25527.85 1149210200 25527.85
## 2019-09-04 25675.16 26654.21 25675.16 26523.23 2688524600 26523.23
## 2019-09-05 26512.86 26697.85 26283.12 26515.53 1996619100 26515.53
## 2019-09-06 26773.13 26790.79 26563.17 26690.76 1895344700 26690.76
## 2019-09-09 26743.36 26807.86 26609.65 26681.40 1700948300 26681.40
## 2019-09-10 26831.98 26870.77 26634.47 26683.68 1738020300 26683.68
## 2019-09-11 26790.64 27159.51 26705.63 27159.06 2072246200 27159.06
## 2019-09-12 27283.98 27283.98 26967.25 27087.63 1337046900 27087.63
## 2019-09-13 27154.51 27366.45 27074.54 27352.69 1176496300 27352.69
## ...
## 2020-08-19 25359.03 25382.51 25079.25 25178.91 1132238600 25178.91
## 2020-08-20 25055.35 25055.35 24621.32 24791.39 1995701500 24791.39
## 2020-08-21 25007.13 25178.79 24885.86 25113.84 1210267900 25113.84
## 2020-08-24 25352.79 25551.58 25325.15 25551.58 1352263800 25551.58
## 2020-08-25 25586.99 25621.08 25352.26 25486.22 1350837400 25486.22
## 2020-08-26 25520.41 25603.17 25360.03 25491.79 1319876200 25491.79
## 2020-08-27 25469.77 25469.77 25186.42 25281.15 1701268300 25281.15
## 2020-08-28 25330.77 25749.40 25258.16 25422.06 2065272600 25422.06
## 2020-08-31 25732.49 25847.11 25177.05 25177.05 3195378200 25177.05
## 2020-09-01 25085.67 25254.14 24995.45 25184.85 1729452000 25184.85
##
## Slot "xsubset":
## [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18
## [19] 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36
## [37] 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54
## [55] 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72
## [73] 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90
## [91] 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108
## [109] 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126
## [127] 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144
## [145] 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162
## [163] 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180
## [181] 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198
## [199] 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216
## [217] 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234
## [235] 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249
##
## Slot "name":
## [1] "HSI"
##
## Slot "type":
## [1] "candlesticks"
##
## Slot "passed.args":
## $x
## HSI
##
## $TA
## list()
##
##
## Slot "windows":
## [1] 1
##
## Slot "xrange":
## [1] 1 249
##
## Slot "yrange":
## [1] 21139.26 29174.92
##
## Slot "log.scale":
## [1] FALSE
##
## Slot "length":
## [1] 249
##
## Slot "color.vol":
## [1] TRUE
##
## Slot "multi.col":
## [1] FALSE
##
## Slot "show.vol":
## [1] TRUE
##
## Slot "show.grid":
## [1] TRUE
##
## Slot "line.type":
## [1] "l"
##
## Slot "bar.type":
## [1] "ohlc"
##
## Slot "xlab":
## character(0)
##
## Slot "ylab":
## character(0)
##
## Slot "spacing":
## [1] 3
##
## Slot "width":
## [1] 3
##
## Slot "bp":
## Sep 02\n2019 Oct 02\n2019 Nov 01\n2019 Dec 02\n2019 Jan 02\n2020 Feb 03\n2020
## 1 22 43 64 84 104
## Mar 02\n2020 Apr 01\n2020 May 04\n2020 Jun 01\n2020 Jul 02\n2020 Aug 03\n2020
## 124 146 165 185 206 228
## Sep 01\n2020 Sep 01\n2020
## 249 249
##
## Slot "x.labels":
## [1] "Sep 02\n2019" "Oct 02\n2019" "Nov 01\n2019" "Dec 02\n2019" "Jan 02\n2020"
## [6] "Feb 03\n2020" "Mar 02\n2020" "Apr 01\n2020" "May 04\n2020" "Jun 01\n2020"
## [11] "Jul 02\n2020" "Aug 03\n2020" "Sep 01\n2020" "Sep 01\n2020"
##
## Slot "colors":
## List of 27
## $ fg.col : chr "#666666"
## $ bg.col : chr "#222222"
## $ grid.col : chr "#303030"
## $ border : chr "#666666"
## $ minor.tick : chr "#303030"
## $ major.tick : chr "#AAAAAA"
## $ up.col : chr "#00FF00"
## $ dn.col : chr "#FF9900"
## $ dn.up.col : chr "#00FF00"
## $ up.up.col : chr "#00FF00"
## $ dn.dn.col : chr "#FF9900"
## $ up.dn.col : chr "#FF9900"
## $ up.border : chr "#666666"
## $ dn.border : chr "#666666"
## $ dn.up.border: chr "#666666"
## $ up.up.border: chr "#666666"
## $ dn.dn.border: chr "#666666"
## $ up.dn.border: chr "#666666"
## $ main.col : chr "#999999"
## $ sub.col : chr "#999999"
## $ area : chr "#252525"
## $ fill : chr "#282828"
## $ Expiry : chr "#383838"
## $ BBands.col : chr "red"
## $ BBands.fill : chr "#282828"
## $ BBands :List of 2
## ..$ col : chr "red"
## ..$ fill: chr "#282828"
## $ theme.name : chr "black"
## - attr(*, "class")= chr "chart.theme"
##
## Slot "layout":
## [1] NA
##
## Slot "time.scale":
## [1] "daily"
##
## Slot "minor.ticks":
## [1] TRUE
##
## Slot "major.ticks":
## [1] "auto"
png(file.path(plotpath, "FTSE_stock.png"), width = 800, height = 600)
chartSeries(FTSE, TA = NULL)
addTA(Cl(FTSE), on = 1, col = "blue", lwd = 2)
addTA(Op(FTSE), on = 1, col = "red", lwd = 2)
dev.off()
## quartz_off_screen
## 2
png(file.path(plotpath, "GSPC_stock.png"), width = 800, height = 600)
chartSeries(GSPC, TA = NULL)
addTA(Cl(GSPC), on = 1, col = "blue", lwd = 2)
addTA(Op(GSPC), on = 1, col = "red", lwd = 2)
dev.off()
## quartz_off_screen
## 2
png(file.path(plotpath, "GDAXI_stock.png"), width = 800, height = 600)
chartSeries(GDAXI, TA = NULL)
addTA(Cl(GDAXI), on = 1, col = "blue", lwd = 2)
addTA(Op(GDAXI), on = 1, col = "red", lwd = 2)
dev.off()
## quartz_off_screen
## 2
png(file.path(plotpath, "FTSEMIB_stock.png"), width = 800, height = 600)
chartSeries(FTSEMIB.MI, TA = NULL)
addTA(Cl(FTSEMIB.MI), on = 1, col = "blue", lwd = 2)
addTA(Op(FTSEMIB.MI), on = 1, col = "red", lwd = 2)
dev.off()
## quartz_off_screen
## 2
png(file.path(plotpath, "FCHI_stock.png"), width = 800, height = 600)
chartSeries(FCHI, TA = NULL)
addTA(Cl(FCHI), on = 1, col = "blue", lwd = 2)
addTA(Op(FCHI), on = 1, col = "red", lwd = 2)
dev.off()
## quartz_off_screen
## 2
png(file.path(plotpath, "AXJO_stock.png"), width = 800, height = 600)
chartSeries(AXJO, TA = NULL)
addTA(Cl(AXJO), on = 1, col = "blue", lwd = 2)
addTA(Op(AXJO), on = 1, col = "red", lwd = 2)
dev.off()
## quartz_off_screen
## 2
png(file.path(plotpath, "HSI_stock.png"), width = 800, height = 600)
chartSeries(HSI, TA = NULL)
addTA(Cl(HSI), on = 1, col = "blue", lwd = 2)
addTA(Op(HSI), on = 1, col = "red", lwd = 2)
dev.off()
## quartz_off_screen
## 2
FTSE_models_list <- make_similarity_matrices(dates2use[["FTSE_dates"]],
indices2use[["FTSE_index"]],
vals2use[["FTSE_center"]],
center_value_name_list,
file.path(resultpath, 'FTSE'),
save_out = TRUE)
GSPC_models_list <- make_similarity_matrices(dates2use[["GSPC_dates"]],
indices2use[["GSPC_index"]],
vals2use[["GSPC_center"]],
center_value_name_list,
file.path(resultpath, 'GSPC'),
save_out = TRUE)
GDAXI_models_list <- make_similarity_matrices(dates2use[["GDAXI_dates"]],
indices2use[["GDAXI_index"]],
vals2use[["GDAXI_center"]],
center_value_name_list,
file.path(resultpath, 'GDAXI'),
save_out = TRUE)
FTSEMIB.MI_models_list <- make_similarity_matrices(dates2use[["FTSEMIB.MI_dates"]],
indices2use[["FTSEMIB.MI_index"]],
vals2use[["FTSEMIB.MI_center"]],
center_value_name_list,
file.path(resultpath, 'FTSEMIB'),
save_out = TRUE)
FCHI_models_list <- make_similarity_matrices(dates2use[["FCHI_dates"]],
indices2use[["FCHI_index"]],
vals2use[["FCHI_center"]],
center_value_name_list,
file.path(resultpath, 'FCHI'),
save_out = TRUE)
AXJO_models_list <- make_similarity_matrices(dates2use[["AXJO_dates"]],
indices2use[["AXJO_index"]],
vals2use[["AXJO_center"]],
center_value_name_list,
file.path(resultpath, 'AXJO'),
save_out = TRUE)
HSI_models_list <- make_similarity_matrices(dates2use[["HSI_dates"]],
indices2use[["HSI_index"]],
vals2use[["HSI_center"]],
center_value_name_list,
file.path(resultpath, 'HSI'),
save_out = TRUE)
# OPENING PRICES
FTSE_open_similarity <- compute_single_variable_similarity(
FTSE_df$FTSE.Open,
indices2use[["FTSE_index"]],
"FTSE_open_similarity",
file.path(resultpath, 'FTSE'),
TRUE
)
GSPC_open_similarity <- compute_single_variable_similarity(
GSPC_df$GSPC.Open,
indices2use[["GSPC_index"]],
"GSPC_open_similarity",
file.path(resultpath, 'GSPC'),
TRUE
)
GDAXI_open_similarity <- compute_single_variable_similarity(
GDAXI_df$GDAXI.Open,
indices2use[["GDAXI_index"]],
"GDAXI_open_similarity",
file.path(resultpath, 'GDAXI'),
TRUE
)
FTSEMIB.MI_open_similarity <- compute_single_variable_similarity(
FTSEMIB.MI_df$FTSEMIB.MI.Open,
indices2use[["FTSEMIB.MI_index"]],
"FTSEMIB.MI_open_similarity",
file.path(resultpath, 'FTSEMIB'),
TRUE
)
FCHI_open_similarity <- compute_single_variable_similarity(
FCHI_df$FCHI.Open,
indices2use[["FCHI_index"]],
"FCHI_open_similarity",
file.path(resultpath, 'FCHI'),
TRUE
)
AXJO_open_similarity <- compute_single_variable_similarity(
AXJO_df$AXJO.Open,
indices2use[["AXJO_index"]],
"AXJO_open_similarity",
file.path(resultpath, 'AXJO'),
TRUE
)
HSI_open_similarity <- compute_single_variable_similarity(
HSI_df$HSI.Open,
indices2use[["HSI_index"]],
"HSI_open_similarity",
file.path(resultpath, 'HSI'),
TRUE
)
# CLOSING PRICES
FTSE_close_similarity <- compute_single_variable_similarity(
FTSE_df$FTSE.Close,
indices2use[["FTSE_index"]],
"FTSE_close_similarity",
file.path(resultpath, 'FTSE'),
TRUE
)
GSPC_close_similarity <- compute_single_variable_similarity(
GSPC_df$GSPC.Close,
indices2use[["GSPC_index"]],
"GSPC_close_similarity",
file.path(resultpath, 'GSPC'),
TRUE
)
GDAXI_close_similarity <- compute_single_variable_similarity(
GDAXI_df$GDAXI.Close,
indices2use[["GDAXI_index"]],
"GDAXI_close_similarity",
file.path(resultpath, 'GDAXI'),
TRUE
)
FTSEMIB.MI_close_similarity <- compute_single_variable_similarity(
FTSEMIB.MI_df$FTSEMIB.MI.Close,
indices2use[["FTSEMIB.MI_index"]],
"FTSEMIB.MI_close_similarity",
file.path(resultpath, 'FTSEMIB'),
TRUE
)
FCHI_close_similarity <- compute_single_variable_similarity(
FCHI_df$FCHI.Close,
indices2use[["FCHI_index"]],
"FCHI_close_similarity",
file.path(resultpath, 'FCHI'),
TRUE
)
AXJO_close_similarity <- compute_single_variable_similarity(
AXJO_df$AXJO.Close,
indices2use[["AXJO_index"]],
"AXJO_close_similarity",
file.path(resultpath, 'AXJO'),
TRUE
)
HSI_close_similarity <- compute_single_variable_similarity(
HSI_df$HSI.Close,
indices2use[["HSI_index"]],
"HSI_close_similarity",
file.path(resultpath, 'HSI'),
TRUE
)
FTSE_list <- list(FTSE_open_similarity,
FTSE_close_similarity)
names(FTSE_list) <- c("open", "close")
GSPC_list <- list(GSPC_open_similarity,
GSPC_close_similarity)
names(GSPC_list) <- c("open", "close")
GDAXI_list <- list(GDAXI_open_similarity,
GDAXI_close_similarity)
names(GDAXI_list) <- c("open", "close")
FTSEMIB.MI_list <- list(FTSEMIB.MI_open_similarity,
FTSEMIB.MI_close_similarity)
names(FTSEMIB.MI_list) <- c("open", "close")
FCHI_list <- list(FCHI_open_similarity,
FCHI_close_similarity)
names(FCHI_list) <- c("open", "close")
AXJO_list <- list(AXJO_open_similarity,
AXJO_close_similarity)
names(AXJO_list) <- c("open", "close")
HSI_list <- list(HSI_open_similarity,
HSI_close_similarity)
names(HSI_list) <- c("open", "close")
Compared modelled to real data
mantel_res_FTSE <- mantel_results_models(FTSE_models_list,
FTSE_list,
nperm,
n_behave_models)
mantel_res_FTSE$model <- rownames(mantel_res_FTSE)
# order by increasing R
mantel_res_FTSE <- mantel_res_FTSE[order(mantel_res_FTSE$r),]
mantel_res_FTSE %>% knitr::kable(format = "html") %>% kable_styling()
|
|
p_value
|
r
|
p_value_adjusted
|
model
|
|
behave_divclose
|
0.003996
|
0.0609820
|
0.0039960
|
behave_divclose
|
|
behave_divopen
|
0.003996
|
0.0714001
|
0.0039960
|
behave_divopen
|
|
behave_convopen
|
0.000999
|
0.1332657
|
0.0011239
|
behave_convopen
|
|
behave_convclose
|
0.000999
|
0.1424187
|
0.0011239
|
behave_convclose
|
|
punctuacted_chinaopen
|
0.000999
|
0.4627748
|
0.0011239
|
punctuacted_chinaopen
|
|
punctuacted_chinaclose
|
0.000999
|
0.4742115
|
0.0011239
|
punctuacted_chinaclose
|
|
behave_nnopen
|
0.000999
|
0.6000199
|
0.0011239
|
behave_nnopen
|
|
behave_nnclose
|
0.000999
|
0.6009709
|
0.0011239
|
behave_nnclose
|
|
punctuated_nn_chinaopen
|
0.000999
|
0.6208935
|
0.0011239
|
punctuated_nn_chinaopen
|
|
punctuated_nn_chinaclose
|
0.000999
|
0.6248661
|
0.0011239
|
punctuated_nn_chinaclose
|
|
punctuated_nn_usaclose
|
0.000999
|
0.6849208
|
0.0011239
|
punctuated_nn_usaclose
|
|
punctuated_nn_usaopen
|
0.000999
|
0.6853251
|
0.0011239
|
punctuated_nn_usaopen
|
|
punctuated_nn_whoopen
|
0.000999
|
0.6866273
|
0.0011239
|
punctuated_nn_whoopen
|
|
punctuated_nn_whoclose
|
0.000999
|
0.6866771
|
0.0011239
|
punctuated_nn_whoclose
|
|
punctuacted_usaclose
|
0.000999
|
0.7338806
|
0.0011239
|
punctuacted_usaclose
|
|
punctuacted_usaopen
|
0.000999
|
0.7390414
|
0.0011239
|
punctuacted_usaopen
|
|
punctuacted_whoclose
|
0.000999
|
0.7480523
|
0.0011239
|
punctuacted_whoclose
|
|
punctuacted_whoopen
|
0.000999
|
0.7512667
|
0.0011239
|
punctuacted_whoopen
|
Compare resulting R values
n1 <- nrow(FTSE_df)
n2 <- nrow(FTSE_df)
model_r_comparison_p <- data.frame()
higher_model <- data.frame()
comparison_name <- data.frame()
for (i in 1:nrow(mantel_res_FTSE)){
for (j in 1:nrow(mantel_res_FTSE)){
model_i <- mantel_res_FTSE[i, "model"]
model_j <- mantel_res_FTSE[j, "model"]
comparison_name[i, j] <- paste0(model_i, "_", model_j)
r_i <- mantel_res_FTSE[i, "r"]
r_j <- mantel_res_FTSE[j, "r"]
result <- compare_correlations(r_i,
r_j,
alpha,
n1,
n2)
model_r_comparison_p[i, j] <- result$p_value
higher_model[i, j] <- ifelse(r_i > r_j,
"Yes",
"No")
}}
mat_r <- as.matrix(higher_model)
higher_r <- mat_r[lower.tri(mat_r)]
mat_p <- as.matrix(model_r_comparison_p)
p_values <- mat_p[lower.tri(mat_p)]
mat_name <- as.matrix(comparison_name)
comp_names <- mat_name[lower.tri(mat_name)]
corr_p_values <- p.adjust(p_values, method = "fdr")
met_significance <- ifelse(corr_p_values < alpha, "Yes", "No")
# build symmetric matrix of corrected p-values for plotting
corr_p_matrix <- matrix(0, nrow(mantel_res_FTSE),
nrow(mantel_res_FTSE))
corr_p_matrix[lower.tri(corr_p_matrix)] <- corr_p_values
corr_p_matrix[upper.tri(corr_p_matrix)] <-
t(corr_p_matrix)[upper.tri(corr_p_matrix)]
diag(corr_p_matrix) <- diag(mat_p)
rownames(corr_p_matrix) <- mantel_res_FTSE$model
colnames(corr_p_matrix) <- mantel_res_FTSE$model
print(paste0("Is my matrix symmetric: ", isSymmetric(corr_p_matrix)))
## [1] "Is my matrix symmetric: TRUE"
# create binary matrix of whether p < alpha
binary_sig <- ifelse(corr_p_matrix < alpha, 0, 1)
rownames(binary_sig) <- mantel_res_FTSE$model
colnames(binary_sig) <- mantel_res_FTSE$model
if (isSymmetric(corr_p_matrix)){
melted_mat <- melt(corr_p_matrix)
plot_p <- ggplot(data = melted_mat, aes(x = Var1,
y = Var2,
fill = as.numeric(value))) +
geom_tile() +
scale_fill_gradient2(low = "lightblue",
high = "blue",
mid = "cornflowerblue",
midpoint = 0.25, limit = c(0, 0.5),
space = "Lab",
name="P-values") +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, vjust = 1,
size = 8, hjust = 1)) +
labs(title = "P-values for each comparison",
x = NULL,
y = NULL) +
coord_fixed()
print(plot_p)
ggsave(file.path(plotpath, "stats_plot_FTSE.png"), dpi = 600)
}else{
print("Matrix is not symmetric, I can't plot it. Go back and check your matrix again!")
}
## Saving 7 x 5 in image
# print which comparisons had significantly higher mantel R's
# take the last row
FTSE_row <- binary_sig[nrow(binary_sig),]
FTSE_row <- data.frame(FTSE_row)
FTSE_high_names <- rownames(FTSE_row)[FTSE_row == 1]
message <- paste("The comparisons with the highest R are, in order of least to highest: ",
paste(FTSE_high_names, collapse = ", "))
print(message)
## [1] "The comparisons with the highest R are, in order of least to highest: punctuated_nn_usaclose, punctuated_nn_usaopen, punctuated_nn_whoopen, punctuated_nn_whoclose, punctuacted_usaclose, punctuacted_usaopen, punctuacted_whoclose, punctuacted_whoopen"
FTSE_comparisons <- data.frame("comparison" = comp_names,
"was_r_higher" = higher_r,
"raw_p" = p_values,
"corr_p" = corr_p_values,
"met_significance" = met_significance)
FTSE_comparisons %>%
knitr::kable(format = "html") %>%
kable_styling()
|
comparison
|
was_r_higher
|
raw_p
|
corr_p
|
met_significance
|
|
behave_divopen_behave_divclose
|
Yes
|
0.4533
|
0.4849993
|
No
|
|
behave_convopen_behave_divclose
|
Yes
|
0.2067
|
0.2432700
|
No
|
|
behave_convclose_behave_divclose
|
Yes
|
0.1782
|
0.2113535
|
No
|
|
punctuacted_chinaopen_behave_divclose
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuacted_chinaclose_behave_divclose
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
behave_nnopen_behave_divclose
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
behave_nnclose_behave_divclose
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuated_nn_chinaopen_behave_divclose
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuated_nn_chinaclose_behave_divclose
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuated_nn_usaclose_behave_divclose
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuated_nn_usaopen_behave_divclose
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuated_nn_whoopen_behave_divclose
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuated_nn_whoclose_behave_divclose
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuacted_usaclose_behave_divclose
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuacted_usaopen_behave_divclose
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuacted_whoclose_behave_divclose
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuacted_whoopen_behave_divclose
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
behave_convopen_behave_divopen
|
Yes
|
0.2418
|
0.2802682
|
No
|
|
behave_convclose_behave_divopen
|
Yes
|
0.2104
|
0.2457344
|
No
|
|
punctuacted_chinaopen_behave_divopen
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuacted_chinaclose_behave_divopen
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
behave_nnopen_behave_divopen
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
behave_nnclose_behave_divopen
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuated_nn_chinaopen_behave_divopen
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuated_nn_chinaclose_behave_divopen
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuated_nn_usaclose_behave_divopen
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuated_nn_usaopen_behave_divopen
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuated_nn_whoopen_behave_divopen
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuated_nn_whoclose_behave_divopen
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuacted_usaclose_behave_divopen
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuacted_usaopen_behave_divopen
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuacted_whoclose_behave_divopen
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuacted_whoopen_behave_divopen
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
behave_convclose_behave_convopen
|
Yes
|
0.4584
|
0.4870500
|
No
|
|
punctuacted_chinaopen_behave_convopen
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuacted_chinaclose_behave_convopen
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
behave_nnopen_behave_convopen
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
behave_nnclose_behave_convopen
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuated_nn_chinaopen_behave_convopen
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuated_nn_chinaclose_behave_convopen
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuated_nn_usaclose_behave_convopen
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuated_nn_usaopen_behave_convopen
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuated_nn_whoopen_behave_convopen
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuated_nn_whoclose_behave_convopen
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuacted_usaclose_behave_convopen
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuacted_usaopen_behave_convopen
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuacted_whoclose_behave_convopen
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuacted_whoopen_behave_convopen
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuacted_chinaopen_behave_convclose
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuacted_chinaclose_behave_convclose
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
behave_nnopen_behave_convclose
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
behave_nnclose_behave_convclose
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuated_nn_chinaopen_behave_convclose
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuated_nn_chinaclose_behave_convclose
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuated_nn_usaclose_behave_convclose
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuated_nn_usaopen_behave_convclose
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuated_nn_whoopen_behave_convclose
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuated_nn_whoclose_behave_convclose
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuacted_usaclose_behave_convclose
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuacted_usaopen_behave_convclose
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuacted_whoclose_behave_convclose
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuacted_whoopen_behave_convclose
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuacted_chinaclose_punctuacted_chinaopen
|
Yes
|
0.4348
|
0.4718043
|
No
|
|
behave_nnopen_punctuacted_chinaopen
|
Yes
|
0.0156
|
0.0253915
|
Yes
|
|
behave_nnclose_punctuacted_chinaopen
|
Yes
|
0.0150
|
0.0246774
|
Yes
|
|
punctuated_nn_chinaopen_punctuacted_chinaopen
|
Yes
|
0.0057
|
0.0101407
|
Yes
|
|
punctuated_nn_chinaclose_punctuacted_chinaopen
|
Yes
|
0.0047
|
0.0084600
|
Yes
|
|
punctuated_nn_usaclose_punctuacted_chinaopen
|
Yes
|
1e-04
|
0.0002125
|
Yes
|
|
punctuated_nn_usaopen_punctuacted_chinaopen
|
Yes
|
1e-04
|
0.0002125
|
Yes
|
|
punctuated_nn_whoopen_punctuacted_chinaopen
|
Yes
|
1e-04
|
0.0002125
|
Yes
|
|
punctuated_nn_whoclose_punctuacted_chinaopen
|
Yes
|
1e-04
|
0.0002125
|
Yes
|
|
punctuacted_usaclose_punctuacted_chinaopen
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuacted_usaopen_punctuacted_chinaopen
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuacted_whoclose_punctuacted_chinaopen
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuacted_whoopen_punctuacted_chinaopen
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
behave_nnopen_punctuacted_chinaclose
|
Yes
|
0.0233
|
0.0371344
|
Yes
|
|
behave_nnclose_punctuacted_chinaclose
|
Yes
|
0.0224
|
0.0360758
|
Yes
|
|
punctuated_nn_chinaopen_punctuacted_chinaclose
|
Yes
|
0.0091
|
0.0153000
|
Yes
|
|
punctuated_nn_chinaclose_punctuacted_chinaclose
|
Yes
|
0.0074
|
0.0128659
|
Yes
|
|
punctuated_nn_usaclose_punctuacted_chinaclose
|
Yes
|
1e-04
|
0.0002125
|
Yes
|
|
punctuated_nn_usaopen_punctuacted_chinaclose
|
Yes
|
1e-04
|
0.0002125
|
Yes
|
|
punctuated_nn_whoopen_punctuacted_chinaclose
|
Yes
|
1e-04
|
0.0002125
|
Yes
|
|
punctuated_nn_whoclose_punctuacted_chinaclose
|
Yes
|
1e-04
|
0.0002125
|
Yes
|
|
punctuacted_usaclose_punctuacted_chinaclose
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuacted_usaopen_punctuacted_chinaclose
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuacted_whoclose_punctuacted_chinaclose
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuacted_whoopen_punctuacted_chinaclose
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
behave_nnclose_behave_nnopen
|
Yes
|
0.4934
|
0.4996000
|
No
|
|
punctuated_nn_chinaopen_behave_nnopen
|
Yes
|
0.3546
|
0.3989250
|
No
|
|
punctuated_nn_chinaclose_behave_nnopen
|
Yes
|
0.3280
|
0.3773233
|
No
|
|
punctuated_nn_usaclose_behave_nnopen
|
Yes
|
0.0520
|
0.0780000
|
No
|
|
punctuated_nn_usaopen_behave_nnopen
|
Yes
|
0.0511
|
0.0774089
|
No
|
|
punctuated_nn_whoopen_behave_nnopen
|
Yes
|
0.0482
|
0.0752510
|
No
|
|
punctuated_nn_whoclose_behave_nnopen
|
Yes
|
0.0481
|
0.0752510
|
No
|
|
punctuacted_usaclose_behave_nnopen
|
Yes
|
0.0031
|
0.0059288
|
Yes
|
|
punctuacted_usaopen_behave_nnopen
|
Yes
|
0.0021
|
0.0041727
|
Yes
|
|
punctuacted_whoclose_behave_nnopen
|
Yes
|
0.0010
|
0.0020400
|
Yes
|
|
punctuacted_whoopen_behave_nnopen
|
Yes
|
8e-04
|
0.0016541
|
Yes
|
|
punctuated_nn_chinaopen_behave_nnclose
|
Yes
|
0.3609
|
0.4017913
|
No
|
|
punctuated_nn_chinaclose_behave_nnclose
|
Yes
|
0.3340
|
0.3785333
|
No
|
|
punctuated_nn_usaclose_behave_nnclose
|
Yes
|
0.0538
|
0.0791481
|
No
|
|
punctuated_nn_usaopen_behave_nnclose
|
Yes
|
0.0528
|
0.0784311
|
No
|
|
punctuated_nn_whoopen_behave_nnclose
|
Yes
|
0.0499
|
0.0763470
|
No
|
|
punctuated_nn_whoclose_behave_nnclose
|
Yes
|
0.0498
|
0.0763470
|
No
|
|
punctuacted_usaclose_behave_nnclose
|
Yes
|
0.0033
|
0.0060831
|
Yes
|
|
punctuacted_usaopen_behave_nnclose
|
Yes
|
0.0022
|
0.0043154
|
Yes
|
|
punctuacted_whoclose_behave_nnclose
|
Yes
|
0.0011
|
0.0022145
|
Yes
|
|
punctuacted_whoopen_behave_nnclose
|
Yes
|
8e-04
|
0.0016541
|
Yes
|
|
punctuated_nn_chinaclose_punctuated_nn_chinaopen
|
Yes
|
0.4710
|
0.4935822
|
No
|
|
punctuated_nn_usaclose_punctuated_nn_chinaopen
|
Yes
|
0.1051
|
0.1386233
|
No
|
|
punctuated_nn_usaopen_punctuated_nn_chinaopen
|
Yes
|
0.1035
|
0.1377000
|
No
|
|
punctuated_nn_whoopen_punctuated_nn_chinaopen
|
Yes
|
0.0986
|
0.1323316
|
No
|
|
punctuated_nn_whoclose_punctuated_nn_chinaopen
|
Yes
|
0.0985
|
0.1323316
|
No
|
|
punctuacted_usaclose_punctuated_nn_chinaopen
|
Yes
|
0.0091
|
0.0153000
|
Yes
|
|
punctuacted_usaopen_punctuated_nn_chinaopen
|
Yes
|
0.0065
|
0.0114310
|
Yes
|
|
punctuacted_whoclose_punctuated_nn_chinaopen
|
Yes
|
0.0033
|
0.0060831
|
Yes
|
|
punctuacted_whoopen_punctuated_nn_chinaopen
|
Yes
|
0.0026
|
0.0050354
|
Yes
|
|
punctuated_nn_usaclose_punctuated_nn_chinaclose
|
Yes
|
0.1189
|
0.1467073
|
No
|
|
punctuated_nn_usaopen_punctuated_nn_chinaclose
|
Yes
|
0.1172
|
0.1457854
|
No
|
|
punctuated_nn_whoopen_punctuated_nn_chinaclose
|
Yes
|
0.1119
|
0.1426725
|
No
|
|
punctuated_nn_whoclose_punctuated_nn_chinaclose
|
Yes
|
0.1117
|
0.1426725
|
No
|
|
punctuacted_usaclose_punctuated_nn_chinaclose
|
Yes
|
0.0111
|
0.0184598
|
Yes
|
|
punctuacted_usaopen_punctuated_nn_chinaclose
|
Yes
|
0.0079
|
0.0135809
|
Yes
|
|
punctuacted_whoclose_punctuated_nn_chinaclose
|
Yes
|
0.0042
|
0.0076500
|
Yes
|
|
punctuacted_whoopen_punctuated_nn_chinaclose
|
Yes
|
0.0033
|
0.0060831
|
Yes
|
|
punctuated_nn_usaopen_punctuated_nn_usaclose
|
Yes
|
0.4966
|
0.4996000
|
No
|
|
punctuated_nn_whoopen_punctuated_nn_usaclose
|
Yes
|
0.4856
|
0.4987800
|
No
|
|
punctuated_nn_whoclose_punctuated_nn_usaclose
|
Yes
|
0.4852
|
0.4987800
|
No
|
|
punctuacted_usaclose_punctuated_nn_usaclose
|
Yes
|
0.1343
|
0.1643832
|
No
|
|
punctuacted_usaopen_punctuated_nn_usaclose
|
Yes
|
0.1088
|
0.1422769
|
No
|
|
punctuacted_whoclose_punctuated_nn_usaclose
|
Yes
|
0.0723
|
0.1014853
|
No
|
|
punctuacted_whoopen_punctuated_nn_usaclose
|
Yes
|
0.0617
|
0.0899057
|
No
|
|
punctuated_nn_whoopen_punctuated_nn_usaopen
|
Yes
|
0.4890
|
0.4987800
|
No
|
|
punctuated_nn_whoclose_punctuated_nn_usaopen
|
Yes
|
0.4886
|
0.4987800
|
No
|
|
punctuacted_usaclose_punctuated_nn_usaopen
|
Yes
|
0.1361
|
0.1652643
|
No
|
|
punctuacted_usaopen_punctuated_nn_usaopen
|
Yes
|
0.1104
|
0.1426725
|
No
|
|
punctuacted_whoclose_punctuated_nn_usaopen
|
Yes
|
0.0735
|
0.1022318
|
No
|
|
punctuacted_whoopen_punctuated_nn_usaopen
|
Yes
|
0.0627
|
0.0905009
|
No
|
|
punctuated_nn_whoclose_punctuated_nn_whoopen
|
Yes
|
0.4996
|
0.4996000
|
No
|
|
punctuacted_usaclose_punctuated_nn_whoopen
|
Yes
|
0.1422
|
0.1703320
|
No
|
|
punctuacted_usaopen_punctuated_nn_whoopen
|
Yes
|
0.1157
|
0.1453500
|
No
|
|
punctuacted_whoclose_punctuated_nn_whoopen
|
Yes
|
0.0775
|
0.1060071
|
No
|
|
punctuacted_whoopen_punctuated_nn_whoopen
|
Yes
|
0.0662
|
0.0939250
|
No
|
|
punctuacted_usaclose_punctuated_nn_whoclose
|
Yes
|
0.1425
|
0.1703320
|
No
|
|
punctuacted_usaopen_punctuated_nn_whoclose
|
Yes
|
0.1159
|
0.1453500
|
No
|
|
punctuacted_whoclose_punctuated_nn_whoclose
|
Yes
|
0.0776
|
0.1060071
|
No
|
|
punctuacted_whoopen_punctuated_nn_whoclose
|
Yes
|
0.0663
|
0.0939250
|
No
|
|
punctuacted_usaopen_punctuacted_usaclose
|
Yes
|
0.4497
|
0.4845359
|
No
|
|
punctuacted_whoclose_punctuacted_usaclose
|
Yes
|
0.3624
|
0.4017913
|
No
|
|
punctuacted_whoopen_punctuacted_usaclose
|
Yes
|
0.3320
|
0.3785333
|
No
|
|
punctuacted_whoclose_punctuacted_usaopen
|
Yes
|
0.4107
|
0.4488364
|
No
|
|
punctuacted_whoopen_punctuacted_usaopen
|
Yes
|
0.3790
|
0.4171727
|
No
|
|
punctuacted_whoopen_punctuacted_whoclose
|
Yes
|
0.4672
|
0.4929766
|
No
|
Compare modelled to real data
mantel_res_GSPC <- mantel_results_models(GSPC_models_list,
GSPC_list,
nperm,
n_behave_models)
mantel_res_GSPC$model <- rownames(mantel_res_GSPC)
#order by increasing R
mantel_res_GSPC <- mantel_res_GSPC[order(mantel_res_GSPC$r),]
mantel_res_GSPC %>% knitr::kable(format = "html") %>% kable_styling()
|
|
p_value
|
r
|
p_value_adjusted
|
model
|
|
behave_convclose
|
1.0000000
|
-0.1359293
|
1.0000000
|
behave_convclose
|
|
behave_convopen
|
1.0000000
|
-0.1315663
|
1.0000000
|
behave_convopen
|
|
punctuacted_chinaclose
|
0.3536464
|
0.0083424
|
0.3978521
|
punctuacted_chinaclose
|
|
punctuacted_chinaopen
|
0.2577423
|
0.0138074
|
0.3092907
|
punctuacted_chinaopen
|
|
punctuated_nn_chinaclose
|
0.0019980
|
0.0704115
|
0.0025689
|
punctuated_nn_chinaclose
|
|
punctuated_nn_chinaopen
|
0.0009990
|
0.0710445
|
0.0013832
|
punctuated_nn_chinaopen
|
|
behave_nnopen
|
0.0009990
|
0.1185961
|
0.0013832
|
behave_nnopen
|
|
behave_nnclose
|
0.0009990
|
0.1193444
|
0.0013832
|
behave_nnclose
|
|
punctuated_nn_whoopen
|
0.0009990
|
0.1369379
|
0.0013832
|
punctuated_nn_whoopen
|
|
punctuated_nn_whoclose
|
0.0009990
|
0.1372854
|
0.0013832
|
punctuated_nn_whoclose
|
|
punctuated_nn_usaopen
|
0.0009990
|
0.1384349
|
0.0013832
|
punctuated_nn_usaopen
|
|
punctuated_nn_usaclose
|
0.0009990
|
0.1388339
|
0.0013832
|
punctuated_nn_usaclose
|
|
behave_divopen
|
0.0009990
|
0.1614090
|
0.0013832
|
behave_divopen
|
|
behave_divclose
|
0.0009990
|
0.1676144
|
0.0013832
|
behave_divclose
|
|
punctuacted_usaopen
|
0.0009990
|
0.1734897
|
0.0013832
|
punctuacted_usaopen
|
|
punctuacted_usaclose
|
0.0009990
|
0.1746060
|
0.0013832
|
punctuacted_usaclose
|
|
punctuacted_whoopen
|
0.0009990
|
0.1778952
|
0.0013832
|
punctuacted_whoopen
|
|
punctuacted_whoclose
|
0.0009990
|
0.1788128
|
0.0013832
|
punctuacted_whoclose
|
Compare resulting correlations
n1 <- nrow(GSPC_df)
n2 <- nrow(GSPC_df)
model_r_comparison_p <- data.frame()
higher_model <- data.frame()
comparison_name <- data.frame()
for (i in 1:nrow(mantel_res_GSPC)){
for (j in 1:nrow(mantel_res_GSPC)){
model_i <- mantel_res_GSPC[i, "model"]
model_j <- mantel_res_GSPC[j, "model"]
comparison_name[i, j] <- paste0(model_i, "_", model_j)
r_i <- mantel_res_GSPC[i, "r"]
r_j <- mantel_res_GSPC[j, "r"]
result <- compare_correlations(r_i,
r_j,
alpha,
n1,
n2)
model_r_comparison_p[i, j] <- result$p_value
higher_model[i, j] <- ifelse(r_i > r_j,
"Yes",
"No")
}}
mat_r <- as.matrix(higher_model)
higher_r <- mat_r[lower.tri(mat_r)]
mat_p <- as.matrix(model_r_comparison_p)
p_values <- mat_p[lower.tri(mat_p)]
mat_name <- as.matrix(comparison_name)
comp_names <- mat_name[lower.tri(mat_name)]
corr_p_values <- p.adjust(p_values, method = "fdr")
met_significance <- ifelse(corr_p_values < alpha, "Yes", "No")
# build symmetric matrix of corrected p-values for plotting
corr_p_matrix <- matrix(0, nrow(mantel_res_GSPC),
nrow(mantel_res_GSPC))
corr_p_matrix[lower.tri(corr_p_matrix)] <- corr_p_values
corr_p_matrix[upper.tri(corr_p_matrix)] <-
t(corr_p_matrix)[upper.tri(corr_p_matrix)]
diag(corr_p_matrix) <- diag(mat_p)
rownames(corr_p_matrix) <- mantel_res_GSPC$model
colnames(corr_p_matrix) <- mantel_res_GSPC$model
print(paste0("Is my matrix symmetric: ", isSymmetric(corr_p_matrix)))
## [1] "Is my matrix symmetric: TRUE"
# create binary matrix of whether p < alpha
binary_sig <- ifelse(corr_p_matrix < alpha, 0, 1)
rownames(binary_sig) <- mantel_res_GSPC$model
colnames(binary_sig) <- mantel_res_GSPC$model
if (isSymmetric(corr_p_matrix)){
melted_mat <- melt(corr_p_matrix)
plot_p <- ggplot(data = melted_mat, aes(x = Var1,
y = Var2,
fill = as.numeric(value))) +
geom_tile() +
scale_fill_gradient2(low = "lightblue",
high = "blue",
mid = "cornflowerblue",
midpoint = 0.25, limit = c(0, 0.5),
space = "Lab",
name="P-values") +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, vjust = 1,
size = 8, hjust = 1)) +
labs(title = "P-values for each comparison",
x = NULL,
y = NULL) +
coord_fixed()
print(plot_p)
ggsave(file.path(plotpath, "stats_plot_GSPC.png"), dpi = 600)
}else{
print("Matrix is not symmetric, I can't plot it. Go back and check your matrix again!")
}
## Saving 7 x 5 in image
# print which comparisons had significantly higher mantel R's
# take the last row
GSPC_row <- binary_sig[nrow(binary_sig),]
GSPC_row <- data.frame(GSPC_row)
GSPC_high_names <- rownames(GSPC_row)[GSPC_row == 1]
message <- paste("The comparisons with the highest R are, in order of least to highest: ",
paste(GSPC_high_names, collapse = ", "))
print(message)
## [1] "The comparisons with the highest R are, in order of least to highest: punctuacted_chinaclose, punctuacted_chinaopen, punctuated_nn_chinaclose, punctuated_nn_chinaopen, behave_nnopen, behave_nnclose, punctuated_nn_whoopen, punctuated_nn_whoclose, punctuated_nn_usaopen, punctuated_nn_usaclose, behave_divopen, behave_divclose, punctuacted_usaopen, punctuacted_usaclose, punctuacted_whoopen, punctuacted_whoclose"
GSPC_comparisons <- data.frame("comparison" = comp_names,
"was_r_higher" = higher_r,
"raw_p" = p_values,
"corr_p" = corr_p_values,
"met_significance" = met_significance)
GSPC_comparisons %>%
knitr::kable(format = "html") %>%
kable_styling()
|
comparison
|
was_r_higher
|
raw_p
|
corr_p
|
met_significance
|
|
behave_convopen_behave_convclose
|
Yes
|
0.4802
|
0.4984000
|
No
|
|
punctuacted_chinaclose_behave_convclose
|
Yes
|
0.0524
|
0.1864465
|
No
|
|
punctuacted_chinaopen_behave_convclose
|
Yes
|
0.0461
|
0.1763325
|
No
|
|
punctuated_nn_chinaclose_behave_convclose
|
Yes
|
0.0102
|
0.0600231
|
No
|
|
punctuated_nn_chinaopen_behave_convclose
|
Yes
|
0.0100
|
0.0600231
|
No
|
|
behave_nnopen_behave_convclose
|
Yes
|
0.0021
|
0.0146045
|
Yes
|
|
behave_nnclose_behave_convclose
|
Yes
|
0.0021
|
0.0146045
|
Yes
|
|
punctuated_nn_whoopen_behave_convclose
|
Yes
|
0.0011
|
0.0096632
|
Yes
|
|
punctuated_nn_whoclose_behave_convclose
|
Yes
|
0.0011
|
0.0096632
|
Yes
|
|
punctuated_nn_usaopen_behave_convclose
|
Yes
|
0.0010
|
0.0096632
|
Yes
|
|
punctuated_nn_usaclose_behave_convclose
|
Yes
|
0.0010
|
0.0096632
|
Yes
|
|
behave_divopen_behave_convclose
|
Yes
|
4e-04
|
0.0055636
|
Yes
|
|
behave_divclose_behave_convclose
|
Yes
|
3e-04
|
0.0051000
|
Yes
|
|
punctuacted_usaopen_behave_convclose
|
Yes
|
2e-04
|
0.0051000
|
Yes
|
|
punctuacted_usaclose_behave_convclose
|
Yes
|
2e-04
|
0.0051000
|
Yes
|
|
punctuacted_whoopen_behave_convclose
|
Yes
|
2e-04
|
0.0051000
|
Yes
|
|
punctuacted_whoclose_behave_convclose
|
Yes
|
2e-04
|
0.0051000
|
Yes
|
|
punctuacted_chinaclose_behave_convopen
|
Yes
|
0.0579
|
0.2013341
|
No
|
|
punctuacted_chinaopen_behave_convopen
|
Yes
|
0.0511
|
0.1861500
|
No
|
|
punctuated_nn_chinaclose_behave_convopen
|
Yes
|
0.0117
|
0.0639321
|
No
|
|
punctuated_nn_chinaopen_behave_convopen
|
Yes
|
0.0114
|
0.0639321
|
No
|
|
behave_nnopen_behave_convopen
|
Yes
|
0.0025
|
0.0159375
|
Yes
|
|
behave_nnclose_behave_convopen
|
Yes
|
0.0024
|
0.0159375
|
Yes
|
|
punctuated_nn_whoopen_behave_convopen
|
Yes
|
0.0013
|
0.0099450
|
Yes
|
|
punctuated_nn_whoclose_behave_convopen
|
Yes
|
0.0012
|
0.0096632
|
Yes
|
|
punctuated_nn_usaopen_behave_convopen
|
Yes
|
0.0012
|
0.0096632
|
Yes
|
|
punctuated_nn_usaclose_behave_convopen
|
Yes
|
0.0012
|
0.0096632
|
Yes
|
|
behave_divopen_behave_convopen
|
Yes
|
5e-04
|
0.0063750
|
Yes
|
|
behave_divclose_behave_convopen
|
Yes
|
4e-04
|
0.0055636
|
Yes
|
|
punctuacted_usaopen_behave_convopen
|
Yes
|
3e-04
|
0.0051000
|
Yes
|
|
punctuacted_usaclose_behave_convopen
|
Yes
|
3e-04
|
0.0051000
|
Yes
|
|
punctuacted_whoopen_behave_convopen
|
Yes
|
2e-04
|
0.0051000
|
Yes
|
|
punctuacted_whoclose_behave_convopen
|
Yes
|
2e-04
|
0.0051000
|
Yes
|
|
punctuacted_chinaopen_punctuacted_chinaclose
|
Yes
|
0.4756
|
0.4984000
|
No
|
|
punctuated_nn_chinaclose_punctuacted_chinaclose
|
Yes
|
0.2434
|
0.4659545
|
No
|
|
punctuated_nn_chinaopen_punctuacted_chinaclose
|
Yes
|
0.2412
|
0.4659545
|
No
|
|
behave_nnopen_punctuacted_chinaclose
|
Yes
|
0.1077
|
0.2921344
|
No
|
|
behave_nnclose_punctuacted_chinaclose
|
Yes
|
0.1061
|
0.2921344
|
No
|
|
punctuated_nn_whoopen_punctuacted_chinaclose
|
Yes
|
0.0739
|
0.2355562
|
No
|
|
punctuated_nn_whoclose_punctuacted_chinaclose
|
Yes
|
0.0733
|
0.2355562
|
No
|
|
punctuated_nn_usaopen_punctuacted_chinaclose
|
Yes
|
0.0715
|
0.2355562
|
No
|
|
punctuated_nn_usaclose_punctuacted_chinaclose
|
Yes
|
0.0709
|
0.2355562
|
No
|
|
behave_divopen_punctuacted_chinaclose
|
Yes
|
0.0421
|
0.1651615
|
No
|
|
behave_divclose_punctuacted_chinaclose
|
Yes
|
0.0360
|
0.1488649
|
No
|
|
punctuacted_usaopen_punctuacted_chinaclose
|
Yes
|
0.0310
|
0.1426500
|
No
|
|
punctuacted_usaclose_punctuacted_chinaclose
|
Yes
|
0.0301
|
0.1426500
|
No
|
|
punctuacted_whoopen_punctuacted_chinaclose
|
Yes
|
0.0276
|
0.1407600
|
No
|
|
punctuacted_whoclose_punctuacted_chinaclose
|
Yes
|
0.0269
|
0.1407600
|
No
|
|
punctuated_nn_chinaclose_punctuacted_chinaopen
|
Yes
|
0.2630
|
0.4659545
|
No
|
|
punctuated_nn_chinaopen_punctuacted_chinaopen
|
Yes
|
0.2607
|
0.4659545
|
No
|
|
behave_nnopen_punctuacted_chinaopen
|
Yes
|
0.1194
|
0.2921344
|
No
|
|
behave_nnclose_punctuacted_chinaopen
|
Yes
|
0.1177
|
0.2921344
|
No
|
|
punctuated_nn_whoopen_punctuacted_chinaopen
|
Yes
|
0.0828
|
0.2436231
|
No
|
|
punctuated_nn_whoclose_punctuacted_chinaopen
|
Yes
|
0.0822
|
0.2436231
|
No
|
|
punctuated_nn_usaopen_punctuacted_chinaopen
|
Yes
|
0.0803
|
0.2436231
|
No
|
|
punctuated_nn_usaclose_punctuacted_chinaopen
|
Yes
|
0.0796
|
0.2436231
|
No
|
|
behave_divopen_punctuacted_chinaopen
|
Yes
|
0.0478
|
0.1783756
|
No
|
|
behave_divclose_punctuacted_chinaopen
|
Yes
|
0.0412
|
0.1651615
|
No
|
|
punctuacted_usaopen_punctuacted_chinaopen
|
Yes
|
0.0355
|
0.1488649
|
No
|
|
punctuacted_usaclose_punctuacted_chinaopen
|
Yes
|
0.0345
|
0.1488649
|
No
|
|
punctuacted_whoopen_punctuacted_chinaopen
|
Yes
|
0.0317
|
0.1426500
|
No
|
|
punctuacted_whoclose_punctuacted_chinaopen
|
Yes
|
0.0310
|
0.1426500
|
No
|
|
punctuated_nn_chinaopen_punctuated_nn_chinaclose
|
Yes
|
0.4972
|
0.4984000
|
No
|
|
behave_nnopen_punctuated_nn_chinaclose
|
Yes
|
0.2933
|
0.4721143
|
No
|
|
behave_nnclose_punctuated_nn_chinaclose
|
Yes
|
0.2904
|
0.4721143
|
No
|
|
punctuated_nn_whoopen_punctuated_nn_chinaclose
|
Yes
|
0.2260
|
0.4592013
|
No
|
|
punctuated_nn_whoclose_punctuated_nn_chinaclose
|
Yes
|
0.2248
|
0.4592013
|
No
|
|
punctuated_nn_usaopen_punctuated_nn_chinaclose
|
Yes
|
0.2209
|
0.4592013
|
No
|
|
punctuated_nn_usaclose_punctuated_nn_chinaclose
|
Yes
|
0.2195
|
0.4592013
|
No
|
|
behave_divopen_punctuated_nn_chinaclose
|
Yes
|
0.1510
|
0.3435750
|
No
|
|
behave_divclose_punctuated_nn_chinaclose
|
Yes
|
0.1349
|
0.3164318
|
No
|
|
punctuacted_usaopen_punctuated_nn_chinaclose
|
Yes
|
0.1208
|
0.2921344
|
No
|
|
punctuacted_usaclose_punctuated_nn_chinaclose
|
Yes
|
0.1182
|
0.2921344
|
No
|
|
punctuacted_whoopen_punctuated_nn_chinaclose
|
Yes
|
0.1109
|
0.2921344
|
No
|
|
punctuacted_whoclose_punctuated_nn_chinaclose
|
Yes
|
0.1089
|
0.2921344
|
No
|
|
behave_nnopen_punctuated_nn_chinaopen
|
Yes
|
0.2958
|
0.4721143
|
No
|
|
behave_nnclose_punctuated_nn_chinaopen
|
Yes
|
0.2929
|
0.4721143
|
No
|
|
punctuated_nn_whoopen_punctuated_nn_chinaopen
|
Yes
|
0.2281
|
0.4592013
|
No
|
|
punctuated_nn_whoclose_punctuated_nn_chinaopen
|
Yes
|
0.2269
|
0.4592013
|
No
|
|
punctuated_nn_usaopen_punctuated_nn_chinaopen
|
Yes
|
0.2230
|
0.4592013
|
No
|
|
punctuated_nn_usaclose_punctuated_nn_chinaopen
|
Yes
|
0.2216
|
0.4592013
|
No
|
|
behave_divopen_punctuated_nn_chinaopen
|
Yes
|
0.1527
|
0.3435750
|
No
|
|
behave_divclose_punctuated_nn_chinaopen
|
Yes
|
0.1365
|
0.3164318
|
No
|
|
punctuacted_usaopen_punctuated_nn_chinaopen
|
Yes
|
0.1222
|
0.2921344
|
No
|
|
punctuacted_usaclose_punctuated_nn_chinaopen
|
Yes
|
0.1197
|
0.2921344
|
No
|
|
punctuacted_whoopen_punctuated_nn_chinaopen
|
Yes
|
0.1122
|
0.2921344
|
No
|
|
punctuacted_whoclose_punctuated_nn_chinaopen
|
Yes
|
0.1102
|
0.2921344
|
No
|
|
behave_nnclose_behave_nnopen
|
Yes
|
0.4966
|
0.4984000
|
No
|
|
punctuated_nn_whoopen_behave_nnopen
|
Yes
|
0.4174
|
0.4984000
|
No
|
|
punctuated_nn_whoclose_behave_nnopen
|
Yes
|
0.4159
|
0.4984000
|
No
|
|
punctuated_nn_usaopen_behave_nnopen
|
Yes
|
0.4108
|
0.4984000
|
No
|
|
punctuated_nn_usaclose_behave_nnopen
|
Yes
|
0.4090
|
0.4984000
|
No
|
|
behave_divopen_behave_nnopen
|
Yes
|
0.3127
|
0.4721143
|
No
|
|
behave_divclose_behave_nnopen
|
Yes
|
0.2879
|
0.4721143
|
No
|
|
punctuacted_usaopen_behave_nnopen
|
Yes
|
0.2652
|
0.4659545
|
No
|
|
punctuacted_usaclose_behave_nnopen
|
Yes
|
0.2610
|
0.4659545
|
No
|
|
punctuacted_whoopen_behave_nnopen
|
Yes
|
0.2489
|
0.4659545
|
No
|
|
punctuacted_whoclose_behave_nnopen
|
Yes
|
0.2455
|
0.4659545
|
No
|
|
punctuated_nn_whoopen_behave_nnclose
|
Yes
|
0.4207
|
0.4984000
|
No
|
|
punctuated_nn_whoclose_behave_nnclose
|
Yes
|
0.4192
|
0.4984000
|
No
|
|
punctuated_nn_usaopen_behave_nnclose
|
Yes
|
0.4141
|
0.4984000
|
No
|
|
punctuated_nn_usaclose_behave_nnclose
|
Yes
|
0.4123
|
0.4984000
|
No
|
|
behave_divopen_behave_nnclose
|
Yes
|
0.3157
|
0.4721143
|
No
|
|
behave_divclose_behave_nnclose
|
Yes
|
0.2908
|
0.4721143
|
No
|
|
punctuacted_usaopen_behave_nnclose
|
Yes
|
0.2680
|
0.4659545
|
No
|
|
punctuacted_usaclose_behave_nnclose
|
Yes
|
0.2638
|
0.4659545
|
No
|
|
punctuacted_whoopen_behave_nnclose
|
Yes
|
0.2516
|
0.4659545
|
No
|
|
punctuacted_whoclose_behave_nnclose
|
Yes
|
0.2482
|
0.4659545
|
No
|
|
punctuated_nn_whoclose_punctuated_nn_whoopen
|
Yes
|
0.4984
|
0.4984000
|
No
|
|
punctuated_nn_usaopen_punctuated_nn_whoopen
|
Yes
|
0.4932
|
0.4984000
|
No
|
|
punctuated_nn_usaclose_punctuated_nn_whoopen
|
Yes
|
0.4914
|
0.4984000
|
No
|
|
behave_divopen_punctuated_nn_whoopen
|
Yes
|
0.3898
|
0.4984000
|
No
|
|
behave_divclose_punctuated_nn_whoopen
|
Yes
|
0.3627
|
0.4887947
|
No
|
|
punctuacted_usaopen_punctuated_nn_whoopen
|
Yes
|
0.3377
|
0.4721143
|
No
|
|
punctuacted_usaclose_punctuated_nn_whoopen
|
Yes
|
0.3330
|
0.4721143
|
No
|
|
punctuacted_whoopen_punctuated_nn_whoopen
|
Yes
|
0.3193
|
0.4721143
|
No
|
|
punctuacted_whoclose_punctuated_nn_whoopen
|
Yes
|
0.3155
|
0.4721143
|
No
|
|
punctuated_nn_usaopen_punctuated_nn_whoclose
|
Yes
|
0.4948
|
0.4984000
|
No
|
|
punctuated_nn_usaclose_punctuated_nn_whoclose
|
Yes
|
0.4930
|
0.4984000
|
No
|
|
behave_divopen_punctuated_nn_whoclose
|
Yes
|
0.3913
|
0.4984000
|
No
|
|
behave_divclose_punctuated_nn_whoclose
|
Yes
|
0.3642
|
0.4887947
|
No
|
|
punctuacted_usaopen_punctuated_nn_whoclose
|
Yes
|
0.3391
|
0.4721143
|
No
|
|
punctuacted_usaclose_punctuated_nn_whoclose
|
Yes
|
0.3344
|
0.4721143
|
No
|
|
punctuacted_whoopen_punctuated_nn_whoclose
|
Yes
|
0.3207
|
0.4721143
|
No
|
|
punctuacted_whoclose_punctuated_nn_whoclose
|
Yes
|
0.3169
|
0.4721143
|
No
|
|
punctuated_nn_usaclose_punctuated_nn_usaopen
|
Yes
|
0.4982
|
0.4984000
|
No
|
|
behave_divopen_punctuated_nn_usaopen
|
Yes
|
0.3964
|
0.4984000
|
No
|
|
behave_divclose_punctuated_nn_usaopen
|
Yes
|
0.3692
|
0.4892043
|
No
|
|
punctuacted_usaopen_punctuated_nn_usaopen
|
Yes
|
0.3439
|
0.4721143
|
No
|
|
punctuacted_usaclose_punctuated_nn_usaopen
|
Yes
|
0.3392
|
0.4721143
|
No
|
|
punctuacted_whoopen_punctuated_nn_usaopen
|
Yes
|
0.3254
|
0.4721143
|
No
|
|
punctuacted_whoclose_punctuated_nn_usaopen
|
Yes
|
0.3216
|
0.4721143
|
No
|
|
behave_divopen_punctuated_nn_usaclose
|
Yes
|
0.3981
|
0.4984000
|
No
|
|
behave_divclose_punctuated_nn_usaclose
|
Yes
|
0.3709
|
0.4892043
|
No
|
|
punctuacted_usaopen_punctuated_nn_usaclose
|
Yes
|
0.3456
|
0.4721143
|
No
|
|
punctuacted_usaclose_punctuated_nn_usaclose
|
Yes
|
0.3409
|
0.4721143
|
No
|
|
punctuacted_whoopen_punctuated_nn_usaclose
|
Yes
|
0.3271
|
0.4721143
|
No
|
|
punctuacted_whoclose_punctuated_nn_usaclose
|
Yes
|
0.3233
|
0.4721143
|
No
|
|
behave_divclose_behave_divopen
|
Yes
|
0.4716
|
0.4984000
|
No
|
|
punctuacted_usaopen_behave_divopen
|
Yes
|
0.4447
|
0.4984000
|
No
|
|
punctuacted_usaclose_behave_divopen
|
Yes
|
0.4397
|
0.4984000
|
No
|
|
punctuacted_whoopen_behave_divopen
|
Yes
|
0.4247
|
0.4984000
|
No
|
|
punctuacted_whoclose_behave_divopen
|
Yes
|
0.4206
|
0.4984000
|
No
|
|
punctuacted_usaopen_behave_divclose
|
Yes
|
0.4730
|
0.4984000
|
No
|
|
punctuacted_usaclose_behave_divclose
|
Yes
|
0.4679
|
0.4984000
|
No
|
|
punctuacted_whoopen_behave_divclose
|
Yes
|
0.4528
|
0.4984000
|
No
|
|
punctuacted_whoclose_behave_divclose
|
Yes
|
0.4486
|
0.4984000
|
No
|
|
punctuacted_usaclose_punctuacted_usaopen
|
Yes
|
0.4949
|
0.4984000
|
No
|
|
punctuacted_whoopen_punctuacted_usaopen
|
Yes
|
0.4797
|
0.4984000
|
No
|
|
punctuacted_whoclose_punctuacted_usaopen
|
Yes
|
0.4755
|
0.4984000
|
No
|
|
punctuacted_whoopen_punctuacted_usaclose
|
Yes
|
0.4849
|
0.4984000
|
No
|
|
punctuacted_whoclose_punctuacted_usaclose
|
Yes
|
0.4806
|
0.4984000
|
No
|
|
punctuacted_whoclose_punctuacted_whoopen
|
Yes
|
0.4958
|
0.4984000
|
No
|
Compare modelled to real data
mantel_res_GDAXI <- mantel_results_models(GDAXI_models_list,
GDAXI_list,
nperm,
n_behave_models)
mantel_res_GDAXI$model <- rownames(mantel_res_GDAXI)
# order by increasing R
mantel_res_GDAXI <- mantel_res_GDAXI[order(mantel_res_GDAXI$r),]
mantel_res_GDAXI %>% knitr::kable(format = "html") %>% kable_styling()
|
|
p_value
|
r
|
p_value_adjusted
|
model
|
|
behave_convclose
|
1.000000
|
-0.1230175
|
1.0000000
|
behave_convclose
|
|
behave_convopen
|
1.000000
|
-0.1203879
|
1.0000000
|
behave_convopen
|
|
punctuated_nn_chinaclose
|
0.002997
|
0.0512232
|
0.0044955
|
punctuated_nn_chinaclose
|
|
punctuated_nn_chinaopen
|
0.001998
|
0.0523103
|
0.0032695
|
punctuated_nn_chinaopen
|
|
punctuacted_chinaopen
|
0.007992
|
0.0645632
|
0.0102754
|
punctuacted_chinaopen
|
|
punctuacted_chinaclose
|
0.003996
|
0.0672499
|
0.0055329
|
punctuacted_chinaclose
|
|
behave_divclose
|
0.017982
|
0.0830793
|
0.0202298
|
behave_divclose
|
|
behave_divopen
|
0.013986
|
0.0835362
|
0.0167832
|
behave_divopen
|
|
behave_nnclose
|
0.000999
|
0.0861029
|
0.0017982
|
behave_nnclose
|
|
behave_nnopen
|
0.000999
|
0.0889385
|
0.0017982
|
behave_nnopen
|
|
punctuated_nn_whoclose
|
0.000999
|
0.1245283
|
0.0017982
|
punctuated_nn_whoclose
|
|
punctuated_nn_usaclose
|
0.000999
|
0.1254534
|
0.0017982
|
punctuated_nn_usaclose
|
|
punctuated_nn_whoopen
|
0.000999
|
0.1259713
|
0.0017982
|
punctuated_nn_whoopen
|
|
punctuated_nn_usaopen
|
0.000999
|
0.1269814
|
0.0017982
|
punctuated_nn_usaopen
|
|
punctuacted_usaclose
|
0.000999
|
0.1970712
|
0.0017982
|
punctuacted_usaclose
|
|
punctuacted_usaopen
|
0.000999
|
0.1986824
|
0.0017982
|
punctuacted_usaopen
|
|
punctuacted_whoclose
|
0.000999
|
0.2078983
|
0.0017982
|
punctuacted_whoclose
|
|
punctuacted_whoopen
|
0.000999
|
0.2094143
|
0.0017982
|
punctuacted_whoopen
|
Compare resulting correlations
n1 <- nrow(GDAXI_df)
n2 <- nrow(GDAXI_df)
model_r_comparison_p <- data.frame()
higher_model <- data.frame()
comparison_name <- data.frame()
for (i in 1:nrow(mantel_res_GDAXI)){
for (j in 1:nrow(mantel_res_GDAXI)){
model_i <- mantel_res_GDAXI[i, "model"]
model_j <- mantel_res_GDAXI[j, "model"]
comparison_name[i, j] <- paste0(model_i, "_", model_j)
r_i <- mantel_res_GDAXI[i, "r"]
r_j <- mantel_res_GDAXI[j, "r"]
result <- compare_correlations(r_i,
r_j,
alpha,
n1,
n2)
model_r_comparison_p[i, j] <- result$p_value
higher_model[i, j] <- ifelse(r_i > r_j,
"Yes",
"No")
}}
mat_r <- as.matrix(higher_model)
higher_r <- mat_r[lower.tri(mat_r)]
mat_p <- as.matrix(model_r_comparison_p)
p_values <- mat_p[lower.tri(mat_p)]
mat_name <- as.matrix(comparison_name)
comp_names <- mat_name[lower.tri(mat_name)]
corr_p_values <- p.adjust(p_values, method = "fdr")
met_significance <- ifelse(corr_p_values < alpha, "Yes", "No")
# build symmetric matrix of corrected p-values for plotting
corr_p_matrix <- matrix(0, nrow(mantel_res_GDAXI),
nrow(mantel_res_GDAXI))
corr_p_matrix[lower.tri(corr_p_matrix)] <- corr_p_values
corr_p_matrix[upper.tri(corr_p_matrix)] <-
t(corr_p_matrix)[upper.tri(corr_p_matrix)]
diag(corr_p_matrix) <- diag(mat_p)
rownames(corr_p_matrix) <- mantel_res_GDAXI$model
colnames(corr_p_matrix) <- mantel_res_GDAXI$model
print(paste0("Is my matrix symmetric: ", isSymmetric(corr_p_matrix)))
## [1] "Is my matrix symmetric: TRUE"
# create binary matrix of whether p < alpha
binary_sig <- ifelse(corr_p_matrix < alpha, 0, 1)
rownames(binary_sig) <- mantel_res_GDAXI$model
colnames(binary_sig) <- mantel_res_GDAXI$model
if (isSymmetric(corr_p_matrix)){
melted_mat <- melt(corr_p_matrix)
plot_p <- ggplot(data = melted_mat, aes(x = Var1,
y = Var2,
fill = as.numeric(value))) +
geom_tile() +
scale_fill_gradient2(low = "lightblue",
high = "blue",
mid = "cornflowerblue",
midpoint = 0.25, limit = c(0, 0.5),
space = "Lab",
name="P-values") +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, vjust = 1,
size = 8, hjust = 1)) +
labs(title = "P-values for each comparison",
x = NULL,
y = NULL) +
coord_fixed()
print(plot_p)
ggsave(file.path(plotpath, "stats_plot_GDAXI.png"), dpi = 600)
}else{
print("Matrix is not symmetric, I can't plot it. Go back and check your matrix again!")
}
## Saving 7 x 5 in image
# print which comparisons had significantly higher mantel R's
# take the last row
GDAXI_row <- binary_sig[nrow(binary_sig),]
GDAXI_row <- data.frame(GDAXI_row)
GDAXI_high_names <- rownames(GDAXI_row)[GDAXI_row == 1]
message <- paste("The comparisons with the highest R are, in order of least to highest: ",
paste(GDAXI_high_names, collapse = ", "))
print(message)
## [1] "The comparisons with the highest R are, in order of least to highest: punctuated_nn_chinaclose, punctuated_nn_chinaopen, punctuacted_chinaopen, punctuacted_chinaclose, behave_divclose, behave_divopen, behave_nnclose, behave_nnopen, punctuated_nn_whoclose, punctuated_nn_usaclose, punctuated_nn_whoopen, punctuated_nn_usaopen, punctuacted_usaclose, punctuacted_usaopen, punctuacted_whoclose, punctuacted_whoopen"
GDAXI_comparisons <- data.frame("comparison" = comp_names,
"was_r_higher" = higher_r,
"raw_p" = p_values,
"corr_p" = corr_p_values,
"met_significance" = met_significance)
GDAXI_comparisons %>%
knitr::kable(format = "html") %>%
kable_styling()
|
comparison
|
was_r_higher
|
raw_p
|
corr_p
|
met_significance
|
|
behave_convopen_behave_convclose
|
Yes
|
0.4881
|
0.4980000
|
No
|
|
punctuated_nn_chinaclose_behave_convclose
|
Yes
|
0.0255
|
0.1300500
|
No
|
|
punctuated_nn_chinaopen_behave_convclose
|
Yes
|
0.0248
|
0.1300500
|
No
|
|
punctuacted_chinaopen_behave_convclose
|
Yes
|
0.0178
|
0.1008667
|
No
|
|
punctuacted_chinaclose_behave_convclose
|
Yes
|
0.0165
|
0.1008667
|
No
|
|
behave_divclose_behave_convclose
|
Yes
|
0.0105
|
0.0720375
|
No
|
|
behave_divopen_behave_convclose
|
Yes
|
0.0103
|
0.0720375
|
No
|
|
behave_nnclose_behave_convclose
|
Yes
|
0.0096
|
0.0720375
|
No
|
|
behave_nnopen_behave_convclose
|
Yes
|
0.0088
|
0.0720375
|
No
|
|
punctuated_nn_whoclose_behave_convclose
|
Yes
|
0.0027
|
0.0286875
|
Yes
|
|
punctuated_nn_usaclose_behave_convclose
|
Yes
|
0.0027
|
0.0286875
|
Yes
|
|
punctuated_nn_whoopen_behave_convclose
|
Yes
|
0.0026
|
0.0286875
|
Yes
|
|
punctuated_nn_usaopen_behave_convclose
|
Yes
|
0.0025
|
0.0286875
|
Yes
|
|
punctuacted_usaclose_behave_convclose
|
Yes
|
2e-04
|
0.0038250
|
Yes
|
|
punctuacted_usaopen_behave_convclose
|
Yes
|
1e-04
|
0.0030600
|
Yes
|
|
punctuacted_whoclose_behave_convclose
|
Yes
|
1e-04
|
0.0030600
|
Yes
|
|
punctuacted_whoopen_behave_convclose
|
Yes
|
1e-04
|
0.0030600
|
Yes
|
|
punctuated_nn_chinaclose_behave_convopen
|
Yes
|
0.0273
|
0.1305281
|
No
|
|
punctuated_nn_chinaopen_behave_convopen
|
Yes
|
0.0266
|
0.1305281
|
No
|
|
punctuacted_chinaopen_behave_convopen
|
Yes
|
0.0192
|
0.1049143
|
No
|
|
punctuacted_chinaclose_behave_convopen
|
Yes
|
0.0178
|
0.1008667
|
No
|
|
behave_divclose_behave_convopen
|
Yes
|
0.0113
|
0.0720375
|
No
|
|
behave_divopen_behave_convopen
|
Yes
|
0.0112
|
0.0720375
|
No
|
|
behave_nnclose_behave_convopen
|
Yes
|
0.0104
|
0.0720375
|
No
|
|
behave_nnopen_behave_convopen
|
Yes
|
0.0095
|
0.0720375
|
No
|
|
punctuated_nn_whoclose_behave_convopen
|
Yes
|
0.0030
|
0.0286875
|
Yes
|
|
punctuated_nn_usaclose_behave_convopen
|
Yes
|
0.0029
|
0.0286875
|
Yes
|
|
punctuated_nn_whoopen_behave_convopen
|
Yes
|
0.0029
|
0.0286875
|
Yes
|
|
punctuated_nn_usaopen_behave_convopen
|
Yes
|
0.0028
|
0.0286875
|
Yes
|
|
punctuacted_usaclose_behave_convopen
|
Yes
|
2e-04
|
0.0038250
|
Yes
|
|
punctuacted_usaopen_behave_convopen
|
Yes
|
2e-04
|
0.0038250
|
Yes
|
|
punctuacted_whoclose_behave_convopen
|
Yes
|
1e-04
|
0.0030600
|
Yes
|
|
punctuacted_whoopen_behave_convopen
|
Yes
|
1e-04
|
0.0030600
|
Yes
|
|
punctuated_nn_chinaopen_punctuated_nn_chinaclose
|
Yes
|
0.4951
|
0.4980000
|
No
|
|
punctuacted_chinaopen_punctuated_nn_chinaclose
|
Yes
|
0.4406
|
0.4980000
|
No
|
|
punctuacted_chinaclose_punctuated_nn_chinaclose
|
Yes
|
0.4288
|
0.4980000
|
No
|
|
behave_divclose_punctuated_nn_chinaclose
|
Yes
|
0.3605
|
0.4655025
|
No
|
|
behave_divopen_punctuated_nn_chinaclose
|
Yes
|
0.3586
|
0.4655025
|
No
|
|
behave_nnclose_punctuated_nn_chinaclose
|
Yes
|
0.3479
|
0.4628583
|
No
|
|
behave_nnopen_punctuated_nn_chinaclose
|
Yes
|
0.3362
|
0.4611664
|
No
|
|
punctuated_nn_whoclose_punctuated_nn_chinaclose
|
Yes
|
0.2048
|
0.3661448
|
No
|
|
punctuated_nn_usaclose_punctuated_nn_chinaclose
|
Yes
|
0.2018
|
0.3661448
|
No
|
|
punctuated_nn_whoopen_punctuated_nn_chinaclose
|
Yes
|
0.2002
|
0.3661448
|
No
|
|
punctuated_nn_usaopen_punctuated_nn_chinaclose
|
Yes
|
0.1970
|
0.3661448
|
No
|
|
punctuacted_usaclose_punctuated_nn_chinaclose
|
Yes
|
0.0489
|
0.1868786
|
No
|
|
punctuacted_usaopen_punctuated_nn_chinaclose
|
Yes
|
0.0470
|
0.1868786
|
No
|
|
punctuacted_whoclose_punctuated_nn_chinaclose
|
Yes
|
0.0374
|
0.1632000
|
No
|
|
punctuacted_whoopen_punctuated_nn_chinaclose
|
Yes
|
0.0360
|
0.1632000
|
No
|
|
punctuacted_chinaopen_punctuated_nn_chinaopen
|
Yes
|
0.4454
|
0.4980000
|
No
|
|
punctuacted_chinaclose_punctuated_nn_chinaopen
|
Yes
|
0.4336
|
0.4980000
|
No
|
|
behave_divclose_punctuated_nn_chinaopen
|
Yes
|
0.3651
|
0.4655025
|
No
|
|
behave_divopen_punctuated_nn_chinaopen
|
Yes
|
0.3631
|
0.4655025
|
No
|
|
behave_nnclose_punctuated_nn_chinaopen
|
Yes
|
0.3524
|
0.4648034
|
No
|
|
behave_nnopen_punctuated_nn_chinaopen
|
Yes
|
0.3406
|
0.4611664
|
No
|
|
punctuated_nn_whoclose_punctuated_nn_chinaopen
|
Yes
|
0.2082
|
0.3661448
|
No
|
|
punctuated_nn_usaclose_punctuated_nn_chinaopen
|
Yes
|
0.2053
|
0.3661448
|
No
|
|
punctuated_nn_whoopen_punctuated_nn_chinaopen
|
Yes
|
0.2036
|
0.3661448
|
No
|
|
punctuated_nn_usaopen_punctuated_nn_chinaopen
|
Yes
|
0.2004
|
0.3661448
|
No
|
|
punctuacted_usaclose_punctuated_nn_chinaopen
|
Yes
|
0.0501
|
0.1868786
|
No
|
|
punctuacted_usaopen_punctuated_nn_chinaopen
|
Yes
|
0.0482
|
0.1868786
|
No
|
|
punctuacted_whoclose_punctuated_nn_chinaopen
|
Yes
|
0.0384
|
0.1632000
|
No
|
|
punctuacted_whoopen_punctuated_nn_chinaopen
|
Yes
|
0.0369
|
0.1632000
|
No
|
|
punctuacted_chinaclose_punctuacted_chinaopen
|
Yes
|
0.4880
|
0.4980000
|
No
|
|
behave_divclose_punctuacted_chinaopen
|
Yes
|
0.4177
|
0.4980000
|
No
|
|
behave_divopen_punctuacted_chinaopen
|
Yes
|
0.4157
|
0.4980000
|
No
|
|
behave_nnclose_punctuacted_chinaopen
|
Yes
|
0.4045
|
0.4980000
|
No
|
|
behave_nnopen_punctuacted_chinaopen
|
Yes
|
0.3922
|
0.4959223
|
No
|
|
punctuated_nn_whoclose_punctuacted_chinaopen
|
Yes
|
0.2497
|
0.4121129
|
No
|
|
punctuated_nn_usaclose_punctuacted_chinaopen
|
Yes
|
0.2464
|
0.4121129
|
No
|
|
punctuated_nn_whoopen_punctuacted_chinaopen
|
Yes
|
0.2446
|
0.4121129
|
No
|
|
punctuated_nn_usaopen_punctuacted_chinaopen
|
Yes
|
0.2410
|
0.4121129
|
No
|
|
punctuacted_usaclose_punctuacted_chinaopen
|
Yes
|
0.0659
|
0.2191891
|
No
|
|
punctuacted_usaopen_punctuacted_chinaopen
|
Yes
|
0.0636
|
0.2162400
|
No
|
|
punctuacted_whoclose_punctuacted_chinaopen
|
Yes
|
0.0513
|
0.1868786
|
No
|
|
punctuacted_whoopen_punctuacted_chinaopen
|
Yes
|
0.0494
|
0.1868786
|
No
|
|
behave_divclose_punctuacted_chinaclose
|
Yes
|
0.4295
|
0.4980000
|
No
|
|
behave_divopen_punctuacted_chinaclose
|
Yes
|
0.4275
|
0.4980000
|
No
|
|
behave_nnclose_punctuacted_chinaclose
|
Yes
|
0.4162
|
0.4980000
|
No
|
|
behave_nnopen_punctuacted_chinaclose
|
Yes
|
0.4038
|
0.4980000
|
No
|
|
punctuated_nn_whoclose_punctuacted_chinaclose
|
Yes
|
0.2594
|
0.4134188
|
No
|
|
punctuated_nn_usaclose_punctuacted_chinaclose
|
Yes
|
0.2560
|
0.4122947
|
No
|
|
punctuated_nn_whoopen_punctuacted_chinaclose
|
Yes
|
0.2541
|
0.4122947
|
No
|
|
punctuated_nn_usaopen_punctuacted_chinaclose
|
Yes
|
0.2505
|
0.4121129
|
No
|
|
punctuacted_usaclose_punctuacted_chinaclose
|
Yes
|
0.0699
|
0.2228062
|
No
|
|
punctuacted_usaopen_punctuacted_chinaclose
|
Yes
|
0.0674
|
0.2194085
|
No
|
|
punctuacted_whoclose_punctuacted_chinaclose
|
Yes
|
0.0545
|
0.1895114
|
No
|
|
punctuacted_whoopen_punctuacted_chinaclose
|
Yes
|
0.0526
|
0.1871581
|
No
|
|
behave_divopen_behave_divclose
|
Yes
|
0.4980
|
0.4980000
|
No
|
|
behave_nnclose_behave_divclose
|
Yes
|
0.4864
|
0.4980000
|
No
|
|
behave_nnopen_behave_divclose
|
Yes
|
0.4737
|
0.4980000
|
No
|
|
punctuated_nn_whoclose_behave_divclose
|
Yes
|
0.3200
|
0.4611664
|
No
|
|
punctuated_nn_usaclose_behave_divclose
|
Yes
|
0.3163
|
0.4611664
|
No
|
|
punctuated_nn_whoopen_behave_divclose
|
Yes
|
0.3142
|
0.4611664
|
No
|
|
punctuated_nn_usaopen_behave_divclose
|
Yes
|
0.3102
|
0.4611664
|
No
|
|
punctuacted_usaclose_behave_divclose
|
Yes
|
0.0970
|
0.2496450
|
No
|
|
punctuacted_usaopen_behave_divclose
|
Yes
|
0.0938
|
0.2496450
|
No
|
|
punctuacted_whoclose_behave_divclose
|
Yes
|
0.0771
|
0.2289115
|
No
|
|
punctuacted_whoopen_behave_divclose
|
Yes
|
0.0746
|
0.2289115
|
No
|
|
behave_nnclose_behave_divopen
|
Yes
|
0.4885
|
0.4980000
|
No
|
|
behave_nnopen_behave_divopen
|
Yes
|
0.4758
|
0.4980000
|
No
|
|
punctuated_nn_whoclose_behave_divopen
|
Yes
|
0.3219
|
0.4611664
|
No
|
|
punctuated_nn_usaclose_behave_divopen
|
Yes
|
0.3181
|
0.4611664
|
No
|
|
punctuated_nn_whoopen_behave_divopen
|
Yes
|
0.3160
|
0.4611664
|
No
|
|
punctuated_nn_usaopen_behave_divopen
|
Yes
|
0.3120
|
0.4611664
|
No
|
|
punctuacted_usaclose_behave_divopen
|
Yes
|
0.0979
|
0.2496450
|
No
|
|
punctuacted_usaopen_behave_divopen
|
Yes
|
0.0947
|
0.2496450
|
No
|
|
punctuacted_whoclose_behave_divopen
|
Yes
|
0.0778
|
0.2289115
|
No
|
|
punctuacted_whoopen_behave_divopen
|
Yes
|
0.0753
|
0.2289115
|
No
|
|
behave_nnopen_behave_nnclose
|
Yes
|
0.4873
|
0.4980000
|
No
|
|
punctuated_nn_whoclose_behave_nnclose
|
Yes
|
0.3323
|
0.4611664
|
No
|
|
punctuated_nn_usaclose_behave_nnclose
|
Yes
|
0.3285
|
0.4611664
|
No
|
|
punctuated_nn_whoopen_behave_nnclose
|
Yes
|
0.3264
|
0.4611664
|
No
|
|
punctuated_nn_usaopen_behave_nnclose
|
Yes
|
0.3222
|
0.4611664
|
No
|
|
punctuacted_usaclose_behave_nnclose
|
Yes
|
0.1029
|
0.2539306
|
No
|
|
punctuacted_usaopen_behave_nnclose
|
Yes
|
0.0996
|
0.2498164
|
No
|
|
punctuacted_whoclose_behave_nnclose
|
Yes
|
0.0821
|
0.2326167
|
No
|
|
punctuacted_whoopen_behave_nnclose
|
Yes
|
0.0795
|
0.2295000
|
No
|
|
punctuated_nn_whoclose_behave_nnopen
|
Yes
|
0.3439
|
0.4615500
|
No
|
|
punctuated_nn_usaclose_behave_nnopen
|
Yes
|
0.3401
|
0.4611664
|
No
|
|
punctuated_nn_whoopen_behave_nnopen
|
Yes
|
0.3379
|
0.4611664
|
No
|
|
punctuated_nn_usaopen_behave_nnopen
|
Yes
|
0.3338
|
0.4611664
|
No
|
|
punctuacted_usaclose_behave_nnopen
|
Yes
|
0.1088
|
0.2601000
|
No
|
|
punctuacted_usaopen_behave_nnopen
|
Yes
|
0.1053
|
0.2557286
|
No
|
|
punctuacted_whoclose_behave_nnopen
|
Yes
|
0.0871
|
0.2379696
|
No
|
|
punctuacted_whoopen_behave_nnopen
|
Yes
|
0.0843
|
0.2345073
|
No
|
|
punctuated_nn_usaclose_punctuated_nn_whoclose
|
Yes
|
0.4958
|
0.4980000
|
No
|
|
punctuated_nn_whoopen_punctuated_nn_whoclose
|
Yes
|
0.4935
|
0.4980000
|
No
|
|
punctuated_nn_usaopen_punctuated_nn_whoclose
|
Yes
|
0.4889
|
0.4980000
|
No
|
|
punctuacted_usaclose_punctuated_nn_whoclose
|
Yes
|
0.2029
|
0.3661448
|
No
|
|
punctuacted_usaopen_punctuated_nn_whoclose
|
Yes
|
0.1977
|
0.3661448
|
No
|
|
punctuacted_whoclose_punctuated_nn_whoclose
|
Yes
|
0.1692
|
0.3661448
|
No
|
|
punctuacted_whoopen_punctuated_nn_whoclose
|
Yes
|
0.1648
|
0.3661448
|
No
|
|
punctuated_nn_whoopen_punctuated_nn_usaclose
|
Yes
|
0.4977
|
0.4980000
|
No
|
|
punctuated_nn_usaopen_punctuated_nn_usaclose
|
Yes
|
0.4931
|
0.4980000
|
No
|
|
punctuacted_usaclose_punctuated_nn_usaclose
|
Yes
|
0.2059
|
0.3661448
|
No
|
|
punctuacted_usaopen_punctuated_nn_usaclose
|
Yes
|
0.2006
|
0.3661448
|
No
|
|
punctuacted_whoclose_punctuated_nn_usaclose
|
Yes
|
0.1719
|
0.3661448
|
No
|
|
punctuacted_whoopen_punctuated_nn_usaclose
|
Yes
|
0.1674
|
0.3661448
|
No
|
|
punctuated_nn_usaopen_punctuated_nn_whoopen
|
Yes
|
0.4954
|
0.4980000
|
No
|
|
punctuacted_usaclose_punctuated_nn_whoopen
|
Yes
|
0.2075
|
0.3661448
|
No
|
|
punctuacted_usaopen_punctuated_nn_whoopen
|
Yes
|
0.2022
|
0.3661448
|
No
|
|
punctuacted_whoclose_punctuated_nn_whoopen
|
Yes
|
0.1734
|
0.3661448
|
No
|
|
punctuacted_whoopen_punctuated_nn_whoopen
|
Yes
|
0.1689
|
0.3661448
|
No
|
|
punctuacted_usaclose_punctuated_nn_usaopen
|
Yes
|
0.2108
|
0.3665045
|
No
|
|
punctuacted_usaopen_punctuated_nn_usaopen
|
Yes
|
0.2055
|
0.3661448
|
No
|
|
punctuacted_whoclose_punctuated_nn_usaopen
|
Yes
|
0.1763
|
0.3661448
|
No
|
|
punctuacted_whoopen_punctuated_nn_usaopen
|
Yes
|
0.1718
|
0.3661448
|
No
|
|
punctuacted_usaopen_punctuacted_usaclose
|
Yes
|
0.4925
|
0.4980000
|
No
|
|
punctuacted_whoclose_punctuacted_usaclose
|
Yes
|
0.4499
|
0.4980000
|
No
|
|
punctuacted_whoopen_punctuacted_usaclose
|
Yes
|
0.4429
|
0.4980000
|
No
|
|
punctuacted_whoclose_punctuacted_usaopen
|
Yes
|
0.4573
|
0.4980000
|
No
|
|
punctuacted_whoopen_punctuacted_usaopen
|
Yes
|
0.4503
|
0.4980000
|
No
|
|
punctuacted_whoopen_punctuacted_whoclose
|
Yes
|
0.4929
|
0.4980000
|
No
|
Compared modelled to real data
mantel_res_FTSEMIB.MI <- mantel_results_models(FTSEMIB.MI_models_list,
FTSEMIB.MI_list,
nperm,
n_behave_models)
mantel_res_FTSEMIB.MI$model <- rownames(mantel_res_FTSEMIB.MI)
# order by increasing R
mantel_res_FTSEMIB.MI <- mantel_res_FTSEMIB.MI[order(mantel_res_FTSEMIB.MI$r),]
mantel_res_FTSEMIB.MI %>% knitr::kable(format = "html") %>% kable_styling()
|
|
p_value
|
r
|
p_value_adjusted
|
model
|
|
behave_convopen
|
0.013986
|
0.0588349
|
0.0139860
|
behave_convopen
|
|
behave_convclose
|
0.012987
|
0.0652538
|
0.0137510
|
behave_convclose
|
|
behave_divclose
|
0.002997
|
0.0698259
|
0.0033716
|
behave_divclose
|
|
behave_divopen
|
0.001998
|
0.0787428
|
0.0023976
|
behave_divopen
|
|
punctuacted_chinaopen
|
0.000999
|
0.3415321
|
0.0012844
|
punctuacted_chinaopen
|
|
punctuacted_chinaclose
|
0.000999
|
0.3540740
|
0.0012844
|
punctuacted_chinaclose
|
|
punctuated_nn_chinaopen
|
0.000999
|
0.4947322
|
0.0012844
|
punctuated_nn_chinaopen
|
|
punctuated_nn_chinaclose
|
0.000999
|
0.4990044
|
0.0012844
|
punctuated_nn_chinaclose
|
|
behave_nnopen
|
0.000999
|
0.5018563
|
0.0012844
|
behave_nnopen
|
|
behave_nnclose
|
0.000999
|
0.5024039
|
0.0012844
|
behave_nnclose
|
|
punctuated_nn_usaopen
|
0.000999
|
0.5726162
|
0.0012844
|
punctuated_nn_usaopen
|
|
punctuated_nn_whoopen
|
0.000999
|
0.5727606
|
0.0012844
|
punctuated_nn_whoopen
|
|
punctuated_nn_usaclose
|
0.000999
|
0.5731691
|
0.0012844
|
punctuated_nn_usaclose
|
|
punctuated_nn_whoclose
|
0.000999
|
0.5737623
|
0.0012844
|
punctuated_nn_whoclose
|
|
punctuacted_usaclose
|
0.000999
|
0.6355037
|
0.0012844
|
punctuacted_usaclose
|
|
punctuacted_usaopen
|
0.000999
|
0.6388375
|
0.0012844
|
punctuacted_usaopen
|
|
punctuacted_whoclose
|
0.000999
|
0.6444609
|
0.0012844
|
punctuacted_whoclose
|
|
punctuacted_whoopen
|
0.000999
|
0.6458831
|
0.0012844
|
punctuacted_whoopen
|
Compare resulting R values
n1 <- nrow(FTSEMIB.MI_df)
n2 <- nrow(FTSEMIB.MI_df)
model_r_comparison_p <- data.frame()
higher_model <- data.frame()
comparison_name <- data.frame()
for (i in 1:nrow(mantel_res_FTSEMIB.MI)){
for (j in 1:nrow(mantel_res_FTSEMIB.MI)){
model_i <- mantel_res_FTSEMIB.MI[i, "model"]
model_j <- mantel_res_FTSEMIB.MI[j, "model"]
comparison_name[i, j] <- paste0(model_i, "_", model_j)
r_i <- mantel_res_FTSEMIB.MI[i, "r"]
r_j <- mantel_res_FTSEMIB.MI[j, "r"]
result <- compare_correlations(r_i,
r_j,
alpha,
n1,
n2)
model_r_comparison_p[i, j] <- result$p_value
higher_model[i, j] <- ifelse(r_i > r_j,
"Yes",
"No")
}}
mat_r <- as.matrix(higher_model)
higher_r <- mat_r[lower.tri(mat_r)]
mat_p <- as.matrix(model_r_comparison_p)
p_values <- mat_p[lower.tri(mat_p)]
mat_name <- as.matrix(comparison_name)
comp_names <- mat_name[lower.tri(mat_name)]
corr_p_values <- p.adjust(p_values, method = "fdr")
met_significance <- ifelse(corr_p_values < alpha, "Yes", "No")
# build symmetric matrix of corrected p-values for plotting
corr_p_matrix <- matrix(0, nrow(mantel_res_FTSEMIB.MI),
nrow(mantel_res_FTSEMIB.MI))
corr_p_matrix[lower.tri(corr_p_matrix)] <- corr_p_values
corr_p_matrix[upper.tri(corr_p_matrix)] <-
t(corr_p_matrix)[upper.tri(corr_p_matrix)]
diag(corr_p_matrix) <- diag(mat_p)
rownames(corr_p_matrix) <- mantel_res_FTSEMIB.MI$model
colnames(corr_p_matrix) <- mantel_res_FTSEMIB.MI$model
print(paste0("Is my matrix symmetric: ", isSymmetric(corr_p_matrix)))
## [1] "Is my matrix symmetric: TRUE"
# create binary matrix of whether p < alpha
binary_sig <- ifelse(corr_p_matrix < alpha, 0, 1)
rownames(binary_sig) <- mantel_res_FTSEMIB.MI$model
colnames(binary_sig) <- mantel_res_FTSEMIB.MI$model
if (isSymmetric(corr_p_matrix)){
melted_mat <- melt(corr_p_matrix)
plot_p <- ggplot(data = melted_mat, aes(x = Var1,
y = Var2,
fill = as.numeric(value))) +
geom_tile() +
scale_fill_gradient2(low = "lightblue",
high = "blue",
mid = "cornflowerblue",
midpoint = 0.25, limit = c(0, 0.5),
space = "Lab",
name="P-values") +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, vjust = 1,
size = 8, hjust = 1)) +
labs(title = "P-values for each comparison",
x = NULL,
y = NULL) +
coord_fixed()
print(plot_p)
ggsave(file.path(plotpath, "stats_plot_FTSEMIB.png"), dpi = 600)
}else{
print("Matrix is not symmetric, I can't plot it. Go back and check your matrix again!")
}
## Saving 7 x 5 in image
# print which comparisons had significantly higher mantel R's
# take the last row
FTSEMIB.MI_row <- binary_sig[nrow(binary_sig),]
FTSEMIB.MI_row <- data.frame(FTSEMIB.MI_row)
FTSEMIB.MI_high_names <- rownames(FTSEMIB.MI_row)[FTSEMIB.MI_row == 1]
message <- paste("The comparisons with the highest R are, in order of least to highest: ",
paste(FTSEMIB.MI_high_names, collapse = ", "))
print(message)
## [1] "The comparisons with the highest R are, in order of least to highest: punctuated_nn_usaopen, punctuated_nn_whoopen, punctuated_nn_usaclose, punctuated_nn_whoclose, punctuacted_usaclose, punctuacted_usaopen, punctuacted_whoclose, punctuacted_whoopen"
FTSEMIB.MI_comparisons <- data.frame("comparison" = comp_names,
"was_r_higher" = higher_r,
"raw_p" = p_values,
"corr_p" = corr_p_values,
"met_significance" = met_significance)
FTSEMIB.MI_comparisons %>%
knitr::kable(format = "html") %>%
kable_styling()
|
comparison
|
was_r_higher
|
raw_p
|
corr_p
|
met_significance
|
|
behave_convclose_behave_convopen
|
Yes
|
0.4713
|
0.4990000
|
No
|
|
behave_divclose_behave_convopen
|
Yes
|
0.4509
|
0.4990000
|
No
|
|
behave_divopen_behave_convopen
|
Yes
|
0.4115
|
0.4880581
|
No
|
|
punctuacted_chinaopen_behave_convopen
|
Yes
|
5e-04
|
0.0011953
|
Yes
|
|
punctuacted_chinaclose_behave_convopen
|
Yes
|
3e-04
|
0.0007914
|
Yes
|
|
punctuated_nn_chinaopen_behave_convopen
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuated_nn_chinaclose_behave_convopen
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
behave_nnopen_behave_convopen
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
behave_nnclose_behave_convopen
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuated_nn_usaopen_behave_convopen
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuated_nn_whoopen_behave_convopen
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuated_nn_usaclose_behave_convopen
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuated_nn_whoclose_behave_convopen
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuacted_usaclose_behave_convopen
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuacted_usaopen_behave_convopen
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuacted_whoclose_behave_convopen
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuacted_whoopen_behave_convopen
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
behave_divclose_behave_convclose
|
Yes
|
0.4795
|
0.4990000
|
No
|
|
behave_divopen_behave_convclose
|
Yes
|
0.4398
|
0.4990000
|
No
|
|
punctuacted_chinaopen_behave_convclose
|
Yes
|
6e-04
|
0.0013909
|
Yes
|
|
punctuacted_chinaclose_behave_convclose
|
Yes
|
3e-04
|
0.0007914
|
Yes
|
|
punctuated_nn_chinaopen_behave_convclose
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuated_nn_chinaclose_behave_convclose
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
behave_nnopen_behave_convclose
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
behave_nnclose_behave_convclose
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuated_nn_usaopen_behave_convclose
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuated_nn_whoopen_behave_convclose
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuated_nn_usaclose_behave_convclose
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuated_nn_whoclose_behave_convclose
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuacted_usaclose_behave_convclose
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuacted_usaopen_behave_convclose
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuacted_whoclose_behave_convclose
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuacted_whoopen_behave_convclose
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
behave_divopen_behave_divclose
|
Yes
|
0.4601
|
0.4990000
|
No
|
|
punctuacted_chinaopen_behave_divclose
|
Yes
|
7e-04
|
0.0015985
|
Yes
|
|
punctuacted_chinaclose_behave_divclose
|
Yes
|
4e-04
|
0.0010200
|
Yes
|
|
punctuated_nn_chinaopen_behave_divclose
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuated_nn_chinaclose_behave_divclose
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
behave_nnopen_behave_divclose
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
behave_nnclose_behave_divclose
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuated_nn_usaopen_behave_divclose
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuated_nn_whoopen_behave_divclose
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuated_nn_usaclose_behave_divclose
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuated_nn_whoclose_behave_divclose
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuacted_usaclose_behave_divclose
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuacted_usaopen_behave_divclose
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuacted_whoclose_behave_divclose
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuacted_whoopen_behave_divclose
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuacted_chinaopen_behave_divopen
|
Yes
|
0.0010
|
0.0021250
|
Yes
|
|
punctuacted_chinaclose_behave_divopen
|
Yes
|
6e-04
|
0.0013909
|
Yes
|
|
punctuated_nn_chinaopen_behave_divopen
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuated_nn_chinaclose_behave_divopen
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
behave_nnopen_behave_divopen
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
behave_nnclose_behave_divopen
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuated_nn_usaopen_behave_divopen
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuated_nn_whoopen_behave_divopen
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuated_nn_usaclose_behave_divopen
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuated_nn_whoclose_behave_divopen
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuacted_usaclose_behave_divopen
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuacted_usaopen_behave_divopen
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuacted_whoclose_behave_divopen
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuacted_whoopen_behave_divopen
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuacted_chinaclose_punctuacted_chinaopen
|
Yes
|
0.4366
|
0.4990000
|
No
|
|
punctuated_nn_chinaopen_punctuacted_chinaopen
|
Yes
|
0.0185
|
0.0307663
|
Yes
|
|
punctuated_nn_chinaclose_punctuacted_chinaopen
|
Yes
|
0.0158
|
0.0265648
|
Yes
|
|
behave_nnopen_punctuacted_chinaopen
|
Yes
|
0.0142
|
0.0241400
|
Yes
|
|
behave_nnclose_punctuacted_chinaopen
|
Yes
|
0.0139
|
0.0238955
|
Yes
|
|
punctuated_nn_usaopen_punctuacted_chinaopen
|
Yes
|
5e-04
|
0.0011953
|
Yes
|
|
punctuated_nn_whoopen_punctuacted_chinaopen
|
Yes
|
5e-04
|
0.0011953
|
Yes
|
|
punctuated_nn_usaclose_punctuacted_chinaopen
|
Yes
|
5e-04
|
0.0011953
|
Yes
|
|
punctuated_nn_whoclose_punctuacted_chinaopen
|
Yes
|
4e-04
|
0.0010200
|
Yes
|
|
punctuacted_usaclose_punctuacted_chinaopen
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuacted_usaopen_punctuacted_chinaopen
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuacted_whoclose_punctuacted_chinaopen
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuacted_whoopen_punctuacted_chinaopen
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuated_nn_chinaopen_punctuacted_chinaclose
|
Yes
|
0.0271
|
0.0431906
|
Yes
|
|
punctuated_nn_chinaclose_punctuacted_chinaclose
|
Yes
|
0.0234
|
0.0376863
|
Yes
|
|
behave_nnopen_punctuacted_chinaclose
|
Yes
|
0.0211
|
0.0343436
|
Yes
|
|
behave_nnclose_punctuacted_chinaclose
|
Yes
|
0.0207
|
0.0340548
|
Yes
|
|
punctuated_nn_usaopen_punctuacted_chinaclose
|
Yes
|
8e-04
|
0.0017239
|
Yes
|
|
punctuated_nn_whoopen_punctuacted_chinaclose
|
Yes
|
8e-04
|
0.0017239
|
Yes
|
|
punctuated_nn_usaclose_punctuacted_chinaclose
|
Yes
|
8e-04
|
0.0017239
|
Yes
|
|
punctuated_nn_whoclose_punctuacted_chinaclose
|
Yes
|
8e-04
|
0.0017239
|
Yes
|
|
punctuacted_usaclose_punctuacted_chinaclose
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuacted_usaopen_punctuacted_chinaclose
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuacted_whoclose_punctuacted_chinaclose
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuacted_whoopen_punctuacted_chinaclose
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuated_nn_chinaclose_punctuated_nn_chinaopen
|
Yes
|
0.4747
|
0.4990000
|
No
|
|
behave_nnopen_punctuated_nn_chinaopen
|
Yes
|
0.4578
|
0.4990000
|
No
|
|
behave_nnclose_punctuated_nn_chinaopen
|
Yes
|
0.4546
|
0.4990000
|
No
|
|
punctuated_nn_usaopen_punctuated_nn_chinaopen
|
Yes
|
0.1113
|
0.1576750
|
No
|
|
punctuated_nn_whoopen_punctuated_nn_chinaopen
|
Yes
|
0.1108
|
0.1576750
|
No
|
|
punctuated_nn_usaclose_punctuated_nn_chinaopen
|
Yes
|
0.1095
|
0.1576750
|
No
|
|
punctuated_nn_whoclose_punctuated_nn_chinaopen
|
Yes
|
0.1077
|
0.1569343
|
No
|
|
punctuacted_usaclose_punctuated_nn_chinaopen
|
Yes
|
0.0099
|
0.0184337
|
Yes
|
|
punctuacted_usaopen_punctuated_nn_chinaopen
|
Yes
|
0.0084
|
0.0160650
|
Yes
|
|
punctuacted_whoclose_punctuated_nn_chinaopen
|
Yes
|
0.0062
|
0.0128189
|
Yes
|
|
punctuacted_whoopen_punctuated_nn_chinaopen
|
Yes
|
0.0058
|
0.0121562
|
Yes
|
|
behave_nnopen_punctuated_nn_chinaclose
|
Yes
|
0.4830
|
0.4990000
|
No
|
|
behave_nnclose_punctuated_nn_chinaclose
|
Yes
|
0.4798
|
0.4990000
|
No
|
|
punctuated_nn_usaopen_punctuated_nn_chinaclose
|
Yes
|
0.1238
|
0.1632000
|
No
|
|
punctuated_nn_whoopen_punctuated_nn_chinaclose
|
Yes
|
0.1233
|
0.1632000
|
No
|
|
punctuated_nn_usaclose_punctuated_nn_chinaclose
|
Yes
|
0.1219
|
0.1632000
|
No
|
|
punctuated_nn_whoclose_punctuated_nn_chinaclose
|
Yes
|
0.1199
|
0.1632000
|
No
|
|
punctuacted_usaclose_punctuated_nn_chinaclose
|
Yes
|
0.0117
|
0.0208151
|
Yes
|
|
punctuacted_usaopen_punctuated_nn_chinaclose
|
Yes
|
0.0100
|
0.0184337
|
Yes
|
|
punctuacted_whoclose_punctuated_nn_chinaclose
|
Yes
|
0.0074
|
0.0148974
|
Yes
|
|
punctuacted_whoopen_punctuated_nn_chinaclose
|
Yes
|
0.0069
|
0.0140760
|
Yes
|
|
behave_nnclose_behave_nnopen
|
Yes
|
0.4967
|
0.4990000
|
No
|
|
punctuated_nn_usaopen_behave_nnopen
|
Yes
|
0.1327
|
0.1632000
|
No
|
|
punctuated_nn_whoopen_behave_nnopen
|
Yes
|
0.1322
|
0.1632000
|
No
|
|
punctuated_nn_usaclose_behave_nnopen
|
Yes
|
0.1307
|
0.1632000
|
No
|
|
punctuated_nn_whoclose_behave_nnopen
|
Yes
|
0.1286
|
0.1632000
|
No
|
|
punctuacted_usaclose_behave_nnopen
|
Yes
|
0.0131
|
0.0230379
|
Yes
|
|
punctuacted_usaopen_behave_nnopen
|
Yes
|
0.0111
|
0.0202179
|
Yes
|
|
punctuacted_whoclose_behave_nnopen
|
Yes
|
0.0084
|
0.0160650
|
Yes
|
|
punctuacted_whoopen_behave_nnopen
|
Yes
|
0.0078
|
0.0154962
|
Yes
|
|
punctuated_nn_usaopen_behave_nnclose
|
Yes
|
0.1344
|
0.1632000
|
No
|
|
punctuated_nn_whoopen_behave_nnclose
|
Yes
|
0.1339
|
0.1632000
|
No
|
|
punctuated_nn_usaclose_behave_nnclose
|
Yes
|
0.1325
|
0.1632000
|
No
|
|
punctuated_nn_whoclose_behave_nnclose
|
Yes
|
0.1304
|
0.1632000
|
No
|
|
punctuacted_usaclose_behave_nnclose
|
Yes
|
0.0134
|
0.0232977
|
Yes
|
|
punctuacted_usaopen_behave_nnclose
|
Yes
|
0.0114
|
0.0205200
|
Yes
|
|
punctuacted_whoclose_behave_nnclose
|
Yes
|
0.0086
|
0.0162444
|
Yes
|
|
punctuacted_whoopen_behave_nnclose
|
Yes
|
0.0079
|
0.0154962
|
Yes
|
|
punctuated_nn_whoopen_punctuated_nn_usaopen
|
Yes
|
0.4990
|
0.4990000
|
No
|
|
punctuated_nn_usaclose_punctuated_nn_usaopen
|
Yes
|
0.4963
|
0.4990000
|
No
|
|
punctuated_nn_whoclose_punctuated_nn_usaopen
|
Yes
|
0.4924
|
0.4990000
|
No
|
|
punctuacted_usaclose_punctuated_nn_usaopen
|
Yes
|
0.1337
|
0.1632000
|
No
|
|
punctuacted_usaopen_punctuated_nn_usaopen
|
Yes
|
0.1207
|
0.1632000
|
No
|
|
punctuacted_whoclose_punctuated_nn_usaopen
|
Yes
|
0.1005
|
0.1513500
|
No
|
|
punctuacted_whoopen_punctuated_nn_usaopen
|
Yes
|
0.0958
|
0.1501898
|
No
|
|
punctuated_nn_usaclose_punctuated_nn_whoopen
|
Yes
|
0.4973
|
0.4990000
|
No
|
|
punctuated_nn_whoclose_punctuated_nn_whoopen
|
Yes
|
0.4933
|
0.4990000
|
No
|
|
punctuacted_usaclose_punctuated_nn_whoopen
|
Yes
|
0.1342
|
0.1632000
|
No
|
|
punctuacted_usaopen_punctuated_nn_whoopen
|
Yes
|
0.1211
|
0.1632000
|
No
|
|
punctuacted_whoclose_punctuated_nn_whoopen
|
Yes
|
0.1009
|
0.1513500
|
No
|
|
punctuacted_whoopen_punctuated_nn_whoopen
|
Yes
|
0.0962
|
0.1501898
|
No
|
|
punctuated_nn_whoclose_punctuated_nn_usaclose
|
Yes
|
0.4961
|
0.4990000
|
No
|
|
punctuacted_usaclose_punctuated_nn_usaclose
|
Yes
|
0.1357
|
0.1634811
|
No
|
|
punctuacted_usaopen_punctuated_nn_usaclose
|
Yes
|
0.1225
|
0.1632000
|
No
|
|
punctuacted_whoclose_punctuated_nn_usaclose
|
Yes
|
0.1022
|
0.1518117
|
No
|
|
punctuacted_whoopen_punctuated_nn_usaclose
|
Yes
|
0.0974
|
0.1505273
|
No
|
|
punctuacted_usaclose_punctuated_nn_whoclose
|
Yes
|
0.1379
|
0.1648336
|
No
|
|
punctuacted_usaopen_punctuated_nn_whoclose
|
Yes
|
0.1245
|
0.1632000
|
No
|
|
punctuacted_whoclose_punctuated_nn_whoclose
|
Yes
|
0.1039
|
0.1528529
|
No
|
|
punctuacted_whoopen_punctuated_nn_whoclose
|
Yes
|
0.0991
|
0.1513500
|
No
|
|
punctuacted_usaopen_punctuacted_usaclose
|
Yes
|
0.4750
|
0.4990000
|
No
|
|
punctuacted_whoclose_punctuacted_usaclose
|
Yes
|
0.4327
|
0.4990000
|
No
|
|
punctuacted_whoopen_punctuacted_usaclose
|
Yes
|
0.4220
|
0.4966615
|
No
|
|
punctuacted_whoclose_punctuacted_usaopen
|
Yes
|
0.4574
|
0.4990000
|
No
|
|
punctuacted_whoopen_punctuacted_usaopen
|
Yes
|
0.4467
|
0.4990000
|
No
|
|
punctuacted_whoopen_punctuacted_whoclose
|
Yes
|
0.4891
|
0.4990000
|
No
|
Compared modelled to real data
mantel_res_FCHI <- mantel_results_models(FCHI_models_list,
FCHI_list,
nperm,
n_behave_models)
mantel_res_FCHI$model <- rownames(mantel_res_FCHI)
# order by increasing R
mantel_res_FCHI <- mantel_res_FCHI[order(mantel_res_FCHI$r),]
mantel_res_FCHI %>% knitr::kable(format = "html") %>% kable_styling()
|
|
p_value
|
r
|
p_value_adjusted
|
model
|
|
behave_divclose
|
0.001998
|
0.0670484
|
0.0021155
|
behave_divclose
|
|
behave_divopen
|
0.003996
|
0.0703231
|
0.0039960
|
behave_divopen
|
|
behave_convopen
|
0.000999
|
0.1024543
|
0.0011239
|
behave_convopen
|
|
behave_convclose
|
0.000999
|
0.1040130
|
0.0011239
|
behave_convclose
|
|
punctuacted_chinaopen
|
0.000999
|
0.3887988
|
0.0011239
|
punctuacted_chinaopen
|
|
punctuacted_chinaclose
|
0.000999
|
0.3941594
|
0.0011239
|
punctuacted_chinaclose
|
|
behave_nnclose
|
0.000999
|
0.5567168
|
0.0011239
|
behave_nnclose
|
|
behave_nnopen
|
0.000999
|
0.5577135
|
0.0011239
|
behave_nnopen
|
|
punctuated_nn_chinaopen
|
0.000999
|
0.5592017
|
0.0011239
|
punctuated_nn_chinaopen
|
|
punctuated_nn_chinaclose
|
0.000999
|
0.5599477
|
0.0011239
|
punctuated_nn_chinaclose
|
|
punctuated_nn_usaclose
|
0.000999
|
0.6311865
|
0.0011239
|
punctuated_nn_usaclose
|
|
punctuated_nn_usaopen
|
0.000999
|
0.6317077
|
0.0011239
|
punctuated_nn_usaopen
|
|
punctuated_nn_whoclose
|
0.000999
|
0.6320874
|
0.0011239
|
punctuated_nn_whoclose
|
|
punctuated_nn_whoopen
|
0.000999
|
0.6324441
|
0.0011239
|
punctuated_nn_whoopen
|
|
punctuacted_usaclose
|
0.000999
|
0.6908659
|
0.0011239
|
punctuacted_usaclose
|
|
punctuacted_usaopen
|
0.000999
|
0.6931904
|
0.0011239
|
punctuacted_usaopen
|
|
punctuacted_whoclose
|
0.000999
|
0.7015777
|
0.0011239
|
punctuacted_whoclose
|
|
punctuacted_whoopen
|
0.000999
|
0.7031929
|
0.0011239
|
punctuacted_whoopen
|
Compare resulting R values
n1 <- nrow(FCHI_df)
n2 <- nrow(FCHI_df)
model_r_comparison_p <- data.frame()
higher_model <- data.frame()
comparison_name <- data.frame()
for (i in 1:nrow(mantel_res_FCHI)){
for (j in 1:nrow(mantel_res_FCHI)){
model_i <- mantel_res_FCHI[i, "model"]
model_j <- mantel_res_FCHI[j, "model"]
comparison_name[i, j] <- paste0(model_i, "_", model_j)
r_i <- mantel_res_FCHI[i, "r"]
r_j <- mantel_res_FCHI[j, "r"]
result <- compare_correlations(r_i,
r_j,
alpha,
n1,
n2)
model_r_comparison_p[i, j] <- result$p_value
higher_model[i, j] <- ifelse(r_i > r_j,
"Yes",
"No")
}}
mat_r <- as.matrix(higher_model)
higher_r <- mat_r[lower.tri(mat_r)]
mat_p <- as.matrix(model_r_comparison_p)
p_values <- mat_p[lower.tri(mat_p)]
mat_name <- as.matrix(comparison_name)
comp_names <- mat_name[lower.tri(mat_name)]
corr_p_values <- p.adjust(p_values, method = "fdr")
met_significance <- ifelse(corr_p_values < alpha, "Yes", "No")
# build symmetric matrix of corrected p-values for plotting
corr_p_matrix <- matrix(0, nrow(mantel_res_FCHI),
nrow(mantel_res_FCHI))
corr_p_matrix[lower.tri(corr_p_matrix)] <- corr_p_values
corr_p_matrix[upper.tri(corr_p_matrix)] <-
t(corr_p_matrix)[upper.tri(corr_p_matrix)]
diag(corr_p_matrix) <- diag(mat_p)
rownames(corr_p_matrix) <- mantel_res_FCHI$model
colnames(corr_p_matrix) <- mantel_res_FCHI$model
print(paste0("Is my matrix symmetric: ", isSymmetric(corr_p_matrix)))
## [1] "Is my matrix symmetric: TRUE"
# create binary matrix of whether p < alpha
binary_sig <- ifelse(corr_p_matrix < alpha, 0, 1)
rownames(binary_sig) <- mantel_res_FCHI$model
colnames(binary_sig) <- mantel_res_FCHI$model
if (isSymmetric(corr_p_matrix)){
melted_mat <- melt(corr_p_matrix)
plot_p <- ggplot(data = melted_mat, aes(x = Var1,
y = Var2,
fill = as.numeric(value))) +
geom_tile() +
scale_fill_gradient2(low = "lightblue",
high = "blue",
mid = "cornflowerblue",
midpoint = 0.25, limit = c(0, 0.5),
space = "Lab",
name="P-values") +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, vjust = 1,
size = 8, hjust = 1)) +
labs(title = "P-values for each comparison",
x = NULL,
y = NULL) +
coord_fixed()
print(plot_p)
ggsave(file.path(plotpath, "stats_plot_FCHI.png"), dpi = 600)
}else{
print("Matrix is not symmetric, I can't plot it. Go back and check your matrix again!")
}
## Saving 7 x 5 in image
# print which comparisons had significantly higher mantel R's
# take the last row
FCHI_row <- binary_sig[nrow(binary_sig),]
FCHI_row <- data.frame(FCHI_row)
FCHI_high_names <- rownames(FCHI_row)[FCHI_row == 1]
message <- paste("The comparisons with the highest R are, in order of least to highest: ",
paste(FCHI_high_names, collapse = ", "))
print(message)
## [1] "The comparisons with the highest R are, in order of least to highest: punctuated_nn_usaclose, punctuated_nn_usaopen, punctuated_nn_whoclose, punctuated_nn_whoopen, punctuacted_usaclose, punctuacted_usaopen, punctuacted_whoclose, punctuacted_whoopen"
FCHI_comparisons <- data.frame("comparison" = comp_names,
"was_r_higher" = higher_r,
"raw_p" = p_values,
"corr_p" = corr_p_values,
"met_significance" = met_significance)
FCHI_comparisons %>%
knitr::kable(format = "html") %>%
kable_styling()
|
comparison
|
was_r_higher
|
raw_p
|
corr_p
|
met_significance
|
|
behave_divopen_behave_divclose
|
Yes
|
0.4852
|
0.4973000
|
No
|
|
behave_convopen_behave_divclose
|
Yes
|
0.3442
|
0.4050969
|
No
|
|
behave_convclose_behave_divclose
|
Yes
|
0.3377
|
0.4005279
|
No
|
|
punctuacted_chinaopen_behave_divclose
|
Yes
|
1e-04
|
0.0002250
|
Yes
|
|
punctuacted_chinaclose_behave_divclose
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
behave_nnclose_behave_divclose
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
behave_nnopen_behave_divclose
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuated_nn_chinaopen_behave_divclose
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuated_nn_chinaclose_behave_divclose
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuated_nn_usaclose_behave_divclose
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuated_nn_usaopen_behave_divclose
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuated_nn_whoclose_behave_divclose
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuated_nn_whoopen_behave_divclose
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuacted_usaclose_behave_divclose
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuacted_usaopen_behave_divclose
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuacted_whoclose_behave_divclose
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuacted_whoopen_behave_divclose
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
behave_convopen_behave_divopen
|
Yes
|
0.3579
|
0.4148386
|
No
|
|
behave_convclose_behave_divopen
|
Yes
|
0.3513
|
0.4102969
|
No
|
|
punctuacted_chinaopen_behave_divopen
|
Yes
|
1e-04
|
0.0002250
|
Yes
|
|
punctuacted_chinaclose_behave_divopen
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
behave_nnclose_behave_divopen
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
behave_nnopen_behave_divopen
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuated_nn_chinaopen_behave_divopen
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuated_nn_chinaclose_behave_divopen
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuated_nn_usaclose_behave_divopen
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuated_nn_usaopen_behave_divopen
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuated_nn_whoclose_behave_divopen
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuated_nn_whoopen_behave_divopen
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuacted_usaclose_behave_divopen
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuacted_usaopen_behave_divopen
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuacted_whoclose_behave_divopen
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuacted_whoopen_behave_divopen
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
behave_convclose_behave_convopen
|
Yes
|
0.4929
|
0.4973000
|
No
|
|
punctuacted_chinaopen_behave_convopen
|
Yes
|
3e-04
|
0.0006375
|
Yes
|
|
punctuacted_chinaclose_behave_convopen
|
Yes
|
2e-04
|
0.0004371
|
Yes
|
|
behave_nnclose_behave_convopen
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
behave_nnopen_behave_convopen
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuated_nn_chinaopen_behave_convopen
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuated_nn_chinaclose_behave_convopen
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuated_nn_usaclose_behave_convopen
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuated_nn_usaopen_behave_convopen
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuated_nn_whoclose_behave_convopen
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuated_nn_whoopen_behave_convopen
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuacted_usaclose_behave_convopen
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuacted_usaopen_behave_convopen
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuacted_whoclose_behave_convopen
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuacted_whoopen_behave_convopen
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuacted_chinaopen_behave_convclose
|
Yes
|
3e-04
|
0.0006375
|
Yes
|
|
punctuacted_chinaclose_behave_convclose
|
Yes
|
2e-04
|
0.0004371
|
Yes
|
|
behave_nnclose_behave_convclose
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
behave_nnopen_behave_convclose
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuated_nn_chinaopen_behave_convclose
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuated_nn_chinaclose_behave_convclose
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuated_nn_usaclose_behave_convclose
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuated_nn_usaopen_behave_convclose
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuated_nn_whoclose_behave_convclose
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuated_nn_whoopen_behave_convclose
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuacted_usaclose_behave_convclose
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuacted_usaopen_behave_convclose
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuacted_whoclose_behave_convclose
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuacted_whoopen_behave_convclose
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuacted_chinaclose_punctuacted_chinaopen
|
Yes
|
0.4716
|
0.4973000
|
No
|
|
behave_nnclose_punctuacted_chinaopen
|
Yes
|
0.0072
|
0.0121055
|
Yes
|
|
behave_nnopen_punctuacted_chinaopen
|
Yes
|
0.0069
|
0.0118618
|
Yes
|
|
punctuated_nn_chinaopen_punctuacted_chinaopen
|
Yes
|
0.0064
|
0.0112552
|
Yes
|
|
punctuated_nn_chinaclose_punctuacted_chinaopen
|
Yes
|
0.0062
|
0.0112552
|
Yes
|
|
punctuated_nn_usaclose_punctuacted_chinaopen
|
Yes
|
1e-04
|
0.0002250
|
Yes
|
|
punctuated_nn_usaopen_punctuacted_chinaopen
|
Yes
|
1e-04
|
0.0002250
|
Yes
|
|
punctuated_nn_whoclose_punctuacted_chinaopen
|
Yes
|
1e-04
|
0.0002250
|
Yes
|
|
punctuated_nn_whoopen_punctuacted_chinaopen
|
Yes
|
1e-04
|
0.0002250
|
Yes
|
|
punctuacted_usaclose_punctuacted_chinaopen
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuacted_usaopen_punctuacted_chinaopen
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuacted_whoclose_punctuacted_chinaopen
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuacted_whoopen_punctuacted_chinaopen
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
behave_nnclose_punctuacted_chinaclose
|
Yes
|
0.0087
|
0.0138656
|
Yes
|
|
behave_nnopen_punctuacted_chinaclose
|
Yes
|
0.0083
|
0.0133674
|
Yes
|
|
punctuated_nn_chinaopen_punctuacted_chinaclose
|
Yes
|
0.0078
|
0.0126957
|
Yes
|
|
punctuated_nn_chinaclose_punctuacted_chinaclose
|
Yes
|
0.0076
|
0.0125032
|
Yes
|
|
punctuated_nn_usaclose_punctuacted_chinaclose
|
Yes
|
1e-04
|
0.0002250
|
Yes
|
|
punctuated_nn_usaopen_punctuacted_chinaclose
|
Yes
|
1e-04
|
0.0002250
|
Yes
|
|
punctuated_nn_whoclose_punctuacted_chinaclose
|
Yes
|
1e-04
|
0.0002250
|
Yes
|
|
punctuated_nn_whoopen_punctuacted_chinaclose
|
Yes
|
1e-04
|
0.0002250
|
Yes
|
|
punctuacted_usaclose_punctuacted_chinaclose
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuacted_usaopen_punctuacted_chinaclose
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuacted_whoclose_punctuacted_chinaclose
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuacted_whoopen_punctuacted_chinaclose
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
behave_nnopen_behave_nnclose
|
Yes
|
0.4935
|
0.4973000
|
No
|
|
punctuated_nn_chinaopen_behave_nnclose
|
Yes
|
0.4838
|
0.4973000
|
No
|
|
punctuated_nn_chinaclose_behave_nnclose
|
Yes
|
0.4789
|
0.4973000
|
No
|
|
punctuated_nn_usaclose_behave_nnclose
|
Yes
|
0.0973
|
0.1348714
|
No
|
|
punctuated_nn_usaopen_behave_nnclose
|
Yes
|
0.0956
|
0.1348714
|
No
|
|
punctuated_nn_whoclose_behave_nnclose
|
Yes
|
0.0944
|
0.1348714
|
No
|
|
punctuated_nn_whoopen_behave_nnclose
|
Yes
|
0.0933
|
0.1348714
|
No
|
|
punctuacted_usaclose_behave_nnclose
|
Yes
|
0.0064
|
0.0112552
|
Yes
|
|
punctuacted_usaopen_behave_nnclose
|
Yes
|
0.0055
|
0.0103889
|
Yes
|
|
punctuacted_whoclose_behave_nnclose
|
Yes
|
0.0032
|
0.0065280
|
Yes
|
|
punctuacted_whoopen_behave_nnclose
|
Yes
|
0.0029
|
0.0060781
|
Yes
|
|
punctuated_nn_chinaopen_behave_nnopen
|
Yes
|
0.4903
|
0.4973000
|
No
|
|
punctuated_nn_chinaclose_behave_nnopen
|
Yes
|
0.4854
|
0.4973000
|
No
|
|
punctuated_nn_usaclose_behave_nnopen
|
Yes
|
0.1001
|
0.1348714
|
No
|
|
punctuated_nn_usaopen_behave_nnopen
|
Yes
|
0.0984
|
0.1348714
|
No
|
|
punctuated_nn_whoclose_behave_nnopen
|
Yes
|
0.0972
|
0.1348714
|
No
|
|
punctuated_nn_whoopen_behave_nnopen
|
Yes
|
0.0961
|
0.1348714
|
No
|
|
punctuacted_usaclose_behave_nnopen
|
Yes
|
0.0067
|
0.0116489
|
Yes
|
|
punctuacted_usaopen_behave_nnopen
|
Yes
|
0.0058
|
0.0108220
|
Yes
|
|
punctuacted_whoclose_behave_nnopen
|
Yes
|
0.0034
|
0.0066692
|
Yes
|
|
punctuacted_whoopen_behave_nnopen
|
Yes
|
0.0030
|
0.0062027
|
Yes
|
|
punctuated_nn_chinaclose_punctuated_nn_chinaopen
|
Yes
|
0.4951
|
0.4973000
|
No
|
|
punctuated_nn_usaclose_punctuated_nn_chinaopen
|
Yes
|
0.1045
|
0.1348714
|
No
|
|
punctuated_nn_usaopen_punctuated_nn_chinaopen
|
Yes
|
0.1027
|
0.1348714
|
No
|
|
punctuated_nn_whoclose_punctuated_nn_chinaopen
|
Yes
|
0.1014
|
0.1348714
|
No
|
|
punctuated_nn_whoopen_punctuated_nn_chinaopen
|
Yes
|
0.1003
|
0.1348714
|
No
|
|
punctuacted_usaclose_punctuated_nn_chinaopen
|
Yes
|
0.0071
|
0.0120700
|
Yes
|
|
punctuacted_usaopen_punctuated_nn_chinaopen
|
Yes
|
0.0062
|
0.0112552
|
Yes
|
|
punctuacted_whoclose_punctuated_nn_chinaopen
|
Yes
|
0.0036
|
0.0069722
|
Yes
|
|
punctuacted_whoopen_punctuated_nn_chinaopen
|
Yes
|
0.0033
|
0.0066434
|
Yes
|
|
punctuated_nn_usaclose_punctuated_nn_chinaclose
|
Yes
|
0.1067
|
0.1349182
|
No
|
|
punctuated_nn_usaopen_punctuated_nn_chinaclose
|
Yes
|
0.1049
|
0.1348714
|
No
|
|
punctuated_nn_whoclose_punctuated_nn_chinaclose
|
Yes
|
0.1036
|
0.1348714
|
No
|
|
punctuated_nn_whoopen_punctuated_nn_chinaclose
|
Yes
|
0.1024
|
0.1348714
|
No
|
|
punctuacted_usaclose_punctuated_nn_chinaclose
|
Yes
|
0.0074
|
0.0123065
|
Yes
|
|
punctuacted_usaopen_punctuated_nn_chinaclose
|
Yes
|
0.0064
|
0.0112552
|
Yes
|
|
punctuacted_whoclose_punctuated_nn_chinaclose
|
Yes
|
0.0038
|
0.0072675
|
Yes
|
|
punctuacted_whoopen_punctuated_nn_chinaclose
|
Yes
|
0.0034
|
0.0066692
|
Yes
|
|
punctuated_nn_usaopen_punctuated_nn_usaclose
|
Yes
|
0.4961
|
0.4973000
|
No
|
|
punctuated_nn_whoclose_punctuated_nn_usaclose
|
Yes
|
0.4933
|
0.4973000
|
No
|
|
punctuated_nn_whoopen_punctuated_nn_usaclose
|
Yes
|
0.4906
|
0.4973000
|
No
|
|
punctuacted_usaclose_punctuated_nn_usaclose
|
Yes
|
0.1161
|
0.1421064
|
No
|
|
punctuacted_usaopen_punctuated_nn_usaclose
|
Yes
|
0.1066
|
0.1349182
|
No
|
|
punctuacted_whoclose_punctuated_nn_usaclose
|
Yes
|
0.0766
|
0.1160376
|
No
|
|
punctuacted_whoopen_punctuated_nn_usaclose
|
Yes
|
0.0715
|
0.1127784
|
No
|
|
punctuated_nn_whoclose_punctuated_nn_usaopen
|
Yes
|
0.4972
|
0.4973000
|
No
|
|
punctuated_nn_whoopen_punctuated_nn_usaopen
|
Yes
|
0.4945
|
0.4973000
|
No
|
|
punctuacted_usaclose_punctuated_nn_usaopen
|
Yes
|
0.1180
|
0.1432857
|
No
|
|
punctuacted_usaopen_punctuated_nn_usaopen
|
Yes
|
0.1084
|
0.1359443
|
No
|
|
punctuacted_whoclose_punctuated_nn_usaopen
|
Yes
|
0.0780
|
0.1170000
|
No
|
|
punctuacted_whoopen_punctuated_nn_usaopen
|
Yes
|
0.0729
|
0.1138133
|
No
|
|
punctuated_nn_whoopen_punctuated_nn_whoclose
|
Yes
|
0.4973
|
0.4973000
|
No
|
|
punctuacted_usaclose_punctuated_nn_whoclose
|
Yes
|
0.1194
|
0.1438441
|
No
|
|
punctuacted_usaopen_punctuated_nn_whoclose
|
Yes
|
0.1097
|
0.1364561
|
No
|
|
punctuacted_whoclose_punctuated_nn_whoclose
|
Yes
|
0.0790
|
0.1173495
|
No
|
|
punctuacted_whoopen_punctuated_nn_whoclose
|
Yes
|
0.0739
|
0.1142091
|
No
|
|
punctuacted_usaclose_punctuated_nn_whoopen
|
Yes
|
0.1208
|
0.1443938
|
No
|
|
punctuacted_usaopen_punctuated_nn_whoopen
|
Yes
|
0.1110
|
0.1369597
|
No
|
|
punctuacted_whoclose_punctuated_nn_whoopen
|
Yes
|
0.0800
|
0.1176923
|
No
|
|
punctuacted_whoopen_punctuated_nn_whoopen
|
Yes
|
0.0748
|
0.1144440
|
No
|
|
punctuacted_usaopen_punctuacted_usaclose
|
Yes
|
0.4800
|
0.4973000
|
No
|
|
punctuacted_whoclose_punctuacted_usaclose
|
Yes
|
0.4076
|
0.4653940
|
No
|
|
punctuacted_whoopen_punctuacted_usaclose
|
Yes
|
0.3937
|
0.4529030
|
No
|
|
punctuacted_whoclose_punctuacted_usaopen
|
Yes
|
0.4271
|
0.4804875
|
No
|
|
punctuacted_whoopen_punctuacted_usaopen
|
Yes
|
0.4131
|
0.4681800
|
No
|
|
punctuacted_whoopen_punctuacted_whoclose
|
Yes
|
0.4857
|
0.4973000
|
No
|
Compared modelled to real data
mantel_res_AXJO <- mantel_results_models(AXJO_models_list,
AXJO_list,
nperm,
n_behave_models)
mantel_res_AXJO$model <- rownames(mantel_res_AXJO)
# order by increasing R
mantel_res_AXJO <- mantel_res_AXJO[order(mantel_res_AXJO$r),]
mantel_res_AXJO %>% knitr::kable(format = "html") %>% kable_styling()
|
|
p_value
|
r
|
p_value_adjusted
|
model
|
|
behave_convopen
|
0.2317682
|
0.0211062
|
0.2317682
|
behave_convopen
|
|
behave_convclose
|
0.1458541
|
0.0296052
|
0.1544338
|
behave_convclose
|
|
behave_divclose
|
0.0009990
|
0.1160205
|
0.0011239
|
behave_divclose
|
|
behave_divopen
|
0.0009990
|
0.1271245
|
0.0011239
|
behave_divopen
|
|
punctuacted_chinaopen
|
0.0009990
|
0.3869140
|
0.0011239
|
punctuacted_chinaopen
|
|
punctuacted_chinaclose
|
0.0009990
|
0.4003584
|
0.0011239
|
punctuacted_chinaclose
|
|
punctuated_nn_chinaopen
|
0.0009990
|
0.5121504
|
0.0011239
|
punctuated_nn_chinaopen
|
|
behave_nnopen
|
0.0009990
|
0.5153699
|
0.0011239
|
behave_nnopen
|
|
behave_nnclose
|
0.0009990
|
0.5165877
|
0.0011239
|
behave_nnclose
|
|
punctuated_nn_chinaclose
|
0.0009990
|
0.5172837
|
0.0011239
|
punctuated_nn_chinaclose
|
|
punctuated_nn_whoopen
|
0.0009990
|
0.5997349
|
0.0011239
|
punctuated_nn_whoopen
|
|
punctuated_nn_usaopen
|
0.0009990
|
0.6005835
|
0.0011239
|
punctuated_nn_usaopen
|
|
punctuated_nn_whoclose
|
0.0009990
|
0.6010498
|
0.0011239
|
punctuated_nn_whoclose
|
|
punctuated_nn_usaclose
|
0.0009990
|
0.6013874
|
0.0011239
|
punctuated_nn_usaclose
|
|
punctuacted_usaclose
|
0.0009990
|
0.6635678
|
0.0011239
|
punctuacted_usaclose
|
|
punctuacted_usaopen
|
0.0009990
|
0.6671247
|
0.0011239
|
punctuacted_usaopen
|
|
punctuacted_whoclose
|
0.0009990
|
0.6736374
|
0.0011239
|
punctuacted_whoclose
|
|
punctuacted_whoopen
|
0.0009990
|
0.6749587
|
0.0011239
|
punctuacted_whoopen
|
Compare resulting R values
n1 <- nrow(AXJO_df)
n2 <- nrow(AXJO_df)
model_r_comparison_p <- data.frame()
higher_model <- data.frame()
comparison_name <- data.frame()
for (i in 1:nrow(mantel_res_AXJO)){
for (j in 1:nrow(mantel_res_AXJO)){
model_i <- mantel_res_AXJO[i, "model"]
model_j <- mantel_res_AXJO[j, "model"]
comparison_name[i, j] <- paste0(model_i, "_", model_j)
r_i <- mantel_res_AXJO[i, "r"]
r_j <- mantel_res_AXJO[j, "r"]
result <- compare_correlations(r_i,
r_j,
alpha,
n1,
n2)
model_r_comparison_p[i, j] <- result$p_value
higher_model[i, j] <- ifelse(r_i > r_j,
"Yes",
"No")
}}
mat_r <- as.matrix(higher_model)
higher_r <- mat_r[lower.tri(mat_r)]
mat_p <- as.matrix(model_r_comparison_p)
p_values <- mat_p[lower.tri(mat_p)]
mat_name <- as.matrix(comparison_name)
comp_names <- mat_name[lower.tri(mat_name)]
corr_p_values <- p.adjust(p_values, method = "fdr")
met_significance <- ifelse(corr_p_values < alpha, "Yes", "No")
# build symmetric matrix of corrected p-values for plotting
corr_p_matrix <- matrix(0, nrow(mantel_res_AXJO),
nrow(mantel_res_AXJO))
corr_p_matrix[lower.tri(corr_p_matrix)] <- corr_p_values
corr_p_matrix[upper.tri(corr_p_matrix)] <-
t(corr_p_matrix)[upper.tri(corr_p_matrix)]
diag(corr_p_matrix) <- diag(mat_p)
rownames(corr_p_matrix) <- mantel_res_AXJO$model
colnames(corr_p_matrix) <- mantel_res_AXJO$model
print(paste0("Is my matrix symmetric: ", isSymmetric(corr_p_matrix)))
## [1] "Is my matrix symmetric: TRUE"
# create binary matrix of whether p < alpha
binary_sig <- ifelse(corr_p_matrix < alpha, 0, 1)
rownames(binary_sig) <- mantel_res_AXJO$model
colnames(binary_sig) <- mantel_res_AXJO$model
if (isSymmetric(corr_p_matrix)){
melted_mat <- melt(corr_p_matrix)
plot_p <- ggplot(data = melted_mat, aes(x = Var1,
y = Var2,
fill = as.numeric(value))) +
geom_tile() +
scale_fill_gradient2(low = "lightblue",
high = "blue",
mid = "cornflowerblue",
midpoint = 0.25, limit = c(0, 0.5),
space = "Lab",
name="P-values") +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, vjust = 1,
size = 8, hjust = 1)) +
labs(title = "P-values for each comparison",
x = NULL,
y = NULL) +
coord_fixed()
print(plot_p)
ggsave(file.path(plotpath, "stats_plot_AXJO.png"), dpi = 600)
}else{
print("Matrix is not symmetric, I can't plot it. Go back and check your matrix again!")
}
## Saving 7 x 5 in image
# print which comparisons had significantly higher mantel R's
# take the last row
AXJO_row <- binary_sig[nrow(binary_sig),]
AXJO_row <- data.frame(AXJO_row)
AXJO_high_names <- rownames(AXJO_row)[AXJO_row == 1]
message <- paste("The comparisons with the highest R are, in order of least to highest: ",
paste(AXJO_high_names, collapse = ", "))
print(message)
## [1] "The comparisons with the highest R are, in order of least to highest: punctuated_nn_whoopen, punctuated_nn_usaopen, punctuated_nn_whoclose, punctuated_nn_usaclose, punctuacted_usaclose, punctuacted_usaopen, punctuacted_whoclose, punctuacted_whoopen"
AXJO_comparisons <- data.frame("comparison" = comp_names,
"was_r_higher" = higher_r,
"raw_p" = p_values,
"corr_p" = corr_p_values,
"met_significance" = met_significance)
AXJO_comparisons %>%
knitr::kable(format = "html") %>%
kable_styling()
|
comparison
|
was_r_higher
|
raw_p
|
corr_p
|
met_significance
|
|
behave_convclose_behave_convopen
|
Yes
|
0.4620
|
0.4976000
|
No
|
|
behave_divclose_behave_convopen
|
Yes
|
0.1420
|
0.1658473
|
No
|
|
behave_divopen_behave_convopen
|
Yes
|
0.1155
|
0.1409786
|
No
|
|
punctuacted_chinaopen_behave_convopen
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuacted_chinaclose_behave_convopen
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuated_nn_chinaopen_behave_convopen
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
behave_nnopen_behave_convopen
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
behave_nnclose_behave_convopen
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuated_nn_chinaclose_behave_convopen
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuated_nn_whoopen_behave_convopen
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuated_nn_usaopen_behave_convopen
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuated_nn_whoclose_behave_convopen
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuated_nn_usaclose_behave_convopen
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuacted_usaclose_behave_convopen
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuacted_usaopen_behave_convopen
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuacted_whoclose_behave_convopen
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuacted_whoopen_behave_convopen
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
behave_divclose_behave_convclose
|
Yes
|
0.1646
|
0.1907864
|
No
|
|
behave_divopen_behave_convclose
|
Yes
|
0.1352
|
0.1591200
|
No
|
|
punctuacted_chinaopen_behave_convclose
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuacted_chinaclose_behave_convclose
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuated_nn_chinaopen_behave_convclose
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
behave_nnopen_behave_convclose
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
behave_nnclose_behave_convclose
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuated_nn_chinaclose_behave_convclose
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuated_nn_whoopen_behave_convclose
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuated_nn_usaopen_behave_convclose
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuated_nn_whoclose_behave_convclose
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuated_nn_usaclose_behave_convclose
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuacted_usaclose_behave_convclose
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuacted_usaopen_behave_convclose
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuacted_whoclose_behave_convclose
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuacted_whoopen_behave_convclose
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
behave_divopen_behave_divclose
|
Yes
|
0.4497
|
0.4976000
|
No
|
|
punctuacted_chinaopen_behave_divclose
|
Yes
|
5e-04
|
0.0012143
|
Yes
|
|
punctuacted_chinaclose_behave_divclose
|
Yes
|
3e-04
|
0.0007525
|
Yes
|
|
punctuated_nn_chinaopen_behave_divclose
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
behave_nnopen_behave_divclose
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
behave_nnclose_behave_divclose
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuated_nn_chinaclose_behave_divclose
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuated_nn_whoopen_behave_divclose
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuated_nn_usaopen_behave_divclose
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuated_nn_whoclose_behave_divclose
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuated_nn_usaclose_behave_divclose
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuacted_usaclose_behave_divclose
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuacted_usaopen_behave_divclose
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuacted_whoclose_behave_divclose
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuacted_whoopen_behave_divclose
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuacted_chinaopen_behave_divopen
|
Yes
|
8e-04
|
0.0018000
|
Yes
|
|
punctuacted_chinaclose_behave_divopen
|
Yes
|
4e-04
|
0.0009871
|
Yes
|
|
punctuated_nn_chinaopen_behave_divopen
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
behave_nnopen_behave_divopen
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
behave_nnclose_behave_divopen
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuated_nn_chinaclose_behave_divopen
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuated_nn_whoopen_behave_divopen
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuated_nn_usaopen_behave_divopen
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuated_nn_whoclose_behave_divopen
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuated_nn_usaclose_behave_divopen
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuacted_usaclose_behave_divopen
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuacted_usaopen_behave_divopen
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuacted_whoclose_behave_divopen
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuacted_whoopen_behave_divopen
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuacted_chinaclose_punctuacted_chinaopen
|
Yes
|
0.4291
|
0.4863133
|
No
|
|
punctuated_nn_chinaopen_punctuacted_chinaopen
|
Yes
|
0.0386
|
0.0641935
|
No
|
|
behave_nnopen_punctuacted_chinaopen
|
Yes
|
0.0346
|
0.0581736
|
No
|
|
behave_nnclose_punctuacted_chinaopen
|
Yes
|
0.0332
|
0.0564400
|
No
|
|
punctuated_nn_chinaclose_punctuacted_chinaopen
|
Yes
|
0.0324
|
0.0556989
|
No
|
|
punctuated_nn_whoopen_punctuacted_chinaopen
|
Yes
|
7e-04
|
0.0015985
|
Yes
|
|
punctuated_nn_usaopen_punctuacted_chinaopen
|
Yes
|
7e-04
|
0.0015985
|
Yes
|
|
punctuated_nn_whoclose_punctuacted_chinaopen
|
Yes
|
6e-04
|
0.0014123
|
Yes
|
|
punctuated_nn_usaclose_punctuacted_chinaopen
|
Yes
|
6e-04
|
0.0014123
|
Yes
|
|
punctuacted_usaclose_punctuacted_chinaopen
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuacted_usaopen_punctuacted_chinaopen
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuacted_whoclose_punctuacted_chinaopen
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuacted_whoopen_punctuacted_chinaopen
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuated_nn_chinaopen_punctuacted_chinaclose
|
Yes
|
0.0560
|
0.0892500
|
No
|
|
behave_nnopen_punctuacted_chinaclose
|
Yes
|
0.0507
|
0.0816537
|
No
|
|
behave_nnclose_punctuacted_chinaclose
|
Yes
|
0.0488
|
0.0794298
|
No
|
|
punctuated_nn_chinaclose_punctuacted_chinaclose
|
Yes
|
0.0477
|
0.0784742
|
No
|
|
punctuated_nn_whoopen_punctuacted_chinaclose
|
Yes
|
0.0013
|
0.0027625
|
Yes
|
|
punctuated_nn_usaopen_punctuacted_chinaclose
|
Yes
|
0.0012
|
0.0025859
|
Yes
|
|
punctuated_nn_whoclose_punctuacted_chinaclose
|
Yes
|
0.0012
|
0.0025859
|
Yes
|
|
punctuated_nn_usaclose_punctuacted_chinaclose
|
Yes
|
0.0012
|
0.0025859
|
Yes
|
|
punctuacted_usaclose_punctuacted_chinaclose
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuacted_usaopen_punctuacted_chinaclose
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuacted_whoclose_punctuacted_chinaclose
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuacted_whoopen_punctuacted_chinaclose
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
behave_nnopen_punctuated_nn_chinaopen
|
Yes
|
0.4804
|
0.4976000
|
No
|
|
behave_nnclose_punctuated_nn_chinaopen
|
Yes
|
0.4730
|
0.4976000
|
No
|
|
punctuated_nn_chinaclose_punctuated_nn_chinaopen
|
Yes
|
0.4688
|
0.4976000
|
No
|
|
punctuated_nn_whoopen_punctuated_nn_chinaopen
|
Yes
|
0.0768
|
0.1115462
|
No
|
|
punctuated_nn_usaopen_punctuated_nn_chinaopen
|
Yes
|
0.0747
|
0.1115462
|
No
|
|
punctuated_nn_whoclose_punctuated_nn_chinaopen
|
Yes
|
0.0736
|
0.1115462
|
No
|
|
punctuated_nn_usaclose_punctuated_nn_chinaopen
|
Yes
|
0.0727
|
0.1115462
|
No
|
|
punctuacted_usaclose_punctuated_nn_chinaopen
|
Yes
|
0.0044
|
0.0080143
|
Yes
|
|
punctuacted_usaopen_punctuated_nn_chinaopen
|
Yes
|
0.0035
|
0.0066111
|
Yes
|
|
punctuacted_whoclose_punctuated_nn_chinaopen
|
Yes
|
0.0024
|
0.0049622
|
Yes
|
|
punctuacted_whoopen_punctuated_nn_chinaopen
|
Yes
|
0.0022
|
0.0046110
|
Yes
|
|
behave_nnclose_behave_nnopen
|
Yes
|
0.4926
|
0.4976000
|
No
|
|
punctuated_nn_chinaclose_behave_nnopen
|
Yes
|
0.4883
|
0.4976000
|
No
|
|
punctuated_nn_whoopen_behave_nnopen
|
Yes
|
0.0842
|
0.1115462
|
No
|
|
punctuated_nn_usaopen_behave_nnopen
|
Yes
|
0.0819
|
0.1115462
|
No
|
|
punctuated_nn_whoclose_behave_nnopen
|
Yes
|
0.0807
|
0.1115462
|
No
|
|
punctuated_nn_usaclose_behave_nnopen
|
Yes
|
0.0798
|
0.1115462
|
No
|
|
punctuacted_usaclose_behave_nnopen
|
Yes
|
0.0051
|
0.0090733
|
Yes
|
|
punctuacted_usaopen_behave_nnopen
|
Yes
|
0.0041
|
0.0076500
|
Yes
|
|
punctuacted_whoclose_behave_nnopen
|
Yes
|
0.0027
|
0.0053649
|
Yes
|
|
punctuacted_whoopen_behave_nnopen
|
Yes
|
0.0025
|
0.0051000
|
Yes
|
|
punctuated_nn_chinaclose_behave_nnclose
|
Yes
|
0.4957
|
0.4976000
|
No
|
|
punctuated_nn_whoopen_behave_nnclose
|
Yes
|
0.0871
|
0.1119857
|
No
|
|
punctuated_nn_usaopen_behave_nnclose
|
Yes
|
0.0848
|
0.1115462
|
No
|
|
punctuated_nn_whoclose_behave_nnclose
|
Yes
|
0.0835
|
0.1115462
|
No
|
|
punctuated_nn_usaclose_behave_nnclose
|
Yes
|
0.0826
|
0.1115462
|
No
|
|
punctuacted_usaclose_behave_nnclose
|
Yes
|
0.0053
|
0.0093207
|
Yes
|
|
punctuacted_usaopen_behave_nnclose
|
Yes
|
0.0043
|
0.0079265
|
Yes
|
|
punctuacted_whoclose_behave_nnclose
|
Yes
|
0.0029
|
0.0056165
|
Yes
|
|
punctuacted_whoopen_behave_nnclose
|
Yes
|
0.0027
|
0.0053649
|
Yes
|
|
punctuated_nn_whoopen_punctuated_nn_chinaclose
|
Yes
|
0.0888
|
0.1132200
|
No
|
|
punctuated_nn_usaopen_punctuated_nn_chinaclose
|
Yes
|
0.0864
|
0.1119857
|
No
|
|
punctuated_nn_whoclose_punctuated_nn_chinaclose
|
Yes
|
0.0851
|
0.1115462
|
No
|
|
punctuated_nn_usaclose_punctuated_nn_chinaclose
|
Yes
|
0.0842
|
0.1115462
|
No
|
|
punctuacted_usaclose_punctuated_nn_chinaclose
|
Yes
|
0.0055
|
0.0095625
|
Yes
|
|
punctuacted_usaopen_punctuated_nn_chinaclose
|
Yes
|
0.0045
|
0.0081000
|
Yes
|
|
punctuacted_whoclose_punctuated_nn_chinaclose
|
Yes
|
0.0030
|
0.0057375
|
Yes
|
|
punctuacted_whoopen_punctuated_nn_chinaclose
|
Yes
|
0.0028
|
0.0054923
|
Yes
|
|
punctuated_nn_usaopen_punctuated_nn_whoopen
|
Yes
|
0.4941
|
0.4976000
|
No
|
|
punctuated_nn_whoclose_punctuated_nn_whoopen
|
Yes
|
0.4908
|
0.4976000
|
No
|
|
punctuated_nn_usaclose_punctuated_nn_whoopen
|
Yes
|
0.4884
|
0.4976000
|
No
|
|
punctuacted_usaclose_punctuated_nn_whoopen
|
Yes
|
0.1161
|
0.1409786
|
No
|
|
punctuacted_usaopen_punctuated_nn_whoopen
|
Yes
|
0.1027
|
0.1298603
|
No
|
|
punctuacted_whoclose_punctuated_nn_whoopen
|
Yes
|
0.0809
|
0.1115462
|
No
|
|
punctuacted_whoopen_punctuated_nn_whoopen
|
Yes
|
0.0769
|
0.1115462
|
No
|
|
punctuated_nn_whoclose_punctuated_nn_usaopen
|
Yes
|
0.4967
|
0.4976000
|
No
|
|
punctuated_nn_usaclose_punctuated_nn_usaopen
|
Yes
|
0.4944
|
0.4976000
|
No
|
|
punctuacted_usaclose_punctuated_nn_usaopen
|
Yes
|
0.1190
|
0.1433622
|
No
|
|
punctuacted_usaopen_punctuated_nn_usaopen
|
Yes
|
0.1054
|
0.1321820
|
No
|
|
punctuacted_whoclose_punctuated_nn_usaopen
|
Yes
|
0.0832
|
0.1115462
|
No
|
|
punctuacted_whoopen_punctuated_nn_usaopen
|
Yes
|
0.0791
|
0.1115462
|
No
|
|
punctuated_nn_usaclose_punctuated_nn_whoclose
|
Yes
|
0.4976
|
0.4976000
|
No
|
|
punctuacted_usaclose_punctuated_nn_whoclose
|
Yes
|
0.1207
|
0.1442742
|
No
|
|
punctuacted_usaopen_punctuated_nn_whoclose
|
Yes
|
0.1069
|
0.1329732
|
No
|
|
punctuacted_whoclose_punctuated_nn_whoclose
|
Yes
|
0.0844
|
0.1115462
|
No
|
|
punctuacted_whoopen_punctuated_nn_whoclose
|
Yes
|
0.0803
|
0.1115462
|
No
|
|
punctuacted_usaclose_punctuated_nn_usaclose
|
Yes
|
0.1219
|
0.1445791
|
No
|
|
punctuacted_usaopen_punctuated_nn_usaclose
|
Yes
|
0.1080
|
0.1332581
|
No
|
|
punctuacted_whoclose_punctuated_nn_usaclose
|
Yes
|
0.0853
|
0.1115462
|
No
|
|
punctuacted_whoopen_punctuated_nn_usaclose
|
Yes
|
0.0812
|
0.1115462
|
No
|
|
punctuacted_usaopen_punctuacted_usaclose
|
Yes
|
0.4714
|
0.4976000
|
No
|
|
punctuacted_whoclose_punctuacted_usaclose
|
Yes
|
0.4190
|
0.4784104
|
No
|
|
punctuacted_whoopen_punctuacted_usaclose
|
Yes
|
0.4084
|
0.4698135
|
No
|
|
punctuacted_whoclose_punctuacted_usaopen
|
Yes
|
0.4472
|
0.4976000
|
No
|
|
punctuacted_whoopen_punctuacted_usaopen
|
Yes
|
0.4365
|
0.4910625
|
No
|
|
punctuacted_whoopen_punctuacted_whoclose
|
Yes
|
0.4892
|
0.4976000
|
No
|
Compared modelled to real data
mantel_res_HSI <- mantel_results_models(HSI_models_list,
HSI_list,
nperm,
n_behave_models)
mantel_res_HSI$model <- rownames(mantel_res_HSI)
# order by increasing R
mantel_res_HSI <- mantel_res_HSI[order(mantel_res_HSI$r),]
mantel_res_HSI %>% knitr::kable(format = "html") %>% kable_styling()
|
|
p_value
|
r
|
p_value_adjusted
|
model
|
|
behave_divclose
|
0.3116883
|
0.0158237
|
0.3116883
|
behave_divclose
|
|
behave_divopen
|
0.2447552
|
0.0226616
|
0.2591526
|
behave_divopen
|
|
behave_convopen
|
0.0199800
|
0.0617672
|
0.0224775
|
behave_convopen
|
|
behave_convclose
|
0.0059940
|
0.0784762
|
0.0071928
|
behave_convclose
|
|
punctuacted_chinaopen
|
0.0009990
|
0.3285903
|
0.0012844
|
punctuacted_chinaopen
|
|
punctuacted_chinaclose
|
0.0009990
|
0.3459060
|
0.0012844
|
punctuacted_chinaclose
|
|
behave_nnopen
|
0.0009990
|
0.3914384
|
0.0012844
|
behave_nnopen
|
|
punctuated_nn_chinaopen
|
0.0009990
|
0.4111655
|
0.0012844
|
punctuated_nn_chinaopen
|
|
behave_nnclose
|
0.0009990
|
0.4132385
|
0.0012844
|
behave_nnclose
|
|
punctuated_nn_chinaclose
|
0.0009990
|
0.4352474
|
0.0012844
|
punctuated_nn_chinaclose
|
|
punctuated_nn_usaopen
|
0.0009990
|
0.4574210
|
0.0012844
|
punctuated_nn_usaopen
|
|
punctuated_nn_whoopen
|
0.0009990
|
0.4592989
|
0.0012844
|
punctuated_nn_whoopen
|
|
punctuated_nn_usaclose
|
0.0009990
|
0.4797498
|
0.0012844
|
punctuated_nn_usaclose
|
|
punctuated_nn_whoclose
|
0.0009990
|
0.4818334
|
0.0012844
|
punctuated_nn_whoclose
|
|
punctuacted_usaopen
|
0.0009990
|
0.5410522
|
0.0012844
|
punctuacted_usaopen
|
|
punctuacted_whoopen
|
0.0009990
|
0.5511805
|
0.0012844
|
punctuacted_whoopen
|
|
punctuacted_usaclose
|
0.0009990
|
0.5606371
|
0.0012844
|
punctuacted_usaclose
|
|
punctuacted_whoclose
|
0.0009990
|
0.5713019
|
0.0012844
|
punctuacted_whoclose
|
Compare resulting R values
n1 <- nrow(HSI_df)
n2 <- nrow(HSI_df)
model_r_comparison_p <- data.frame()
higher_model <- data.frame()
comparison_name <- data.frame()
for (i in 1:nrow(mantel_res_HSI)){
for (j in 1:nrow(mantel_res_HSI)){
model_i <- mantel_res_HSI[i, "model"]
model_j <- mantel_res_HSI[j, "model"]
comparison_name[i, j] <- paste0(model_i, "_", model_j)
r_i <- mantel_res_HSI[i, "r"]
r_j <- mantel_res_HSI[j, "r"]
result <- compare_correlations(r_i,
r_j,
alpha,
n1,
n2)
model_r_comparison_p[i, j] <- result$p_value
higher_model[i, j] <- ifelse(r_i > r_j,
"Yes",
"No")
}}
mat_r <- as.matrix(higher_model)
higher_r <- mat_r[lower.tri(mat_r)]
mat_p <- as.matrix(model_r_comparison_p)
p_values <- mat_p[lower.tri(mat_p)]
mat_name <- as.matrix(comparison_name)
comp_names <- mat_name[lower.tri(mat_name)]
corr_p_values <- p.adjust(p_values, method = "fdr")
met_significance <- ifelse(corr_p_values < alpha, "Yes", "No")
# build symmetric matrix of corrected p-values for plotting
corr_p_matrix <- matrix(0, nrow(mantel_res_HSI),
nrow(mantel_res_HSI))
corr_p_matrix[lower.tri(corr_p_matrix)] <- corr_p_values
corr_p_matrix[upper.tri(corr_p_matrix)] <-
t(corr_p_matrix)[upper.tri(corr_p_matrix)]
diag(corr_p_matrix) <- diag(mat_p)
rownames(corr_p_matrix) <- mantel_res_HSI$model
colnames(corr_p_matrix) <- mantel_res_HSI$model
print(paste0("Is my matrix symmetric: ", isSymmetric(corr_p_matrix)))
## [1] "Is my matrix symmetric: TRUE"
# create binary matrix of whether p < alpha
binary_sig <- ifelse(corr_p_matrix < alpha, 0, 1)
rownames(binary_sig) <- mantel_res_HSI$model
colnames(binary_sig) <- mantel_res_HSI$model
if (isSymmetric(corr_p_matrix)){
melted_mat <- melt(corr_p_matrix)
plot_p <- ggplot(data = melted_mat, aes(x = Var1,
y = Var2,
fill = as.numeric(value))) +
geom_tile() +
scale_fill_gradient2(low = "lightblue",
high = "blue",
mid = "cornflowerblue",
midpoint = 0.25, limit = c(0, 0.5),
space = "Lab",
name="P-values") +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, vjust = 1,
size = 8, hjust = 1)) +
labs(title = "P-values for each comparison",
x = NULL,
y = NULL) +
coord_fixed()
print(plot_p)
ggsave(file.path(plotpath, "stats_plot_HSI.png"), dpi = 600)
}else{
print("Matrix is not symmetric, I can't plot it. Go back and check your matrix again!")
}
## Saving 7 x 5 in image
# print which comparisons had significantly higher mantel R's
# take the last row
HSI_row <- binary_sig[nrow(binary_sig),]
HSI_row <- data.frame(HSI_row)
HSI_high_names <- rownames(HSI_row)[HSI_row == 1]
message <- paste("The comparisons with the highest R are, in order of least to highest: ",
paste(HSI_high_names, collapse = ", "))
print(message)
## [1] "The comparisons with the highest R are, in order of least to highest: punctuated_nn_usaopen, punctuated_nn_whoopen, punctuated_nn_usaclose, punctuated_nn_whoclose, punctuacted_usaopen, punctuacted_whoopen, punctuacted_usaclose, punctuacted_whoclose"
HSI_comparisons <- data.frame("comparison" = comp_names,
"was_r_higher" = higher_r,
"raw_p" = p_values,
"corr_p" = corr_p_values,
"met_significance" = met_significance)
HSI_comparisons %>%
knitr::kable(format = "html") %>%
kable_styling()
|
comparison
|
was_r_higher
|
raw_p
|
corr_p
|
met_significance
|
|
behave_divopen_behave_divclose
|
Yes
|
0.4698
|
0.4791960
|
No
|
|
behave_convopen_behave_divclose
|
Yes
|
0.3049
|
0.3588438
|
No
|
|
behave_convclose_behave_divclose
|
Yes
|
0.2430
|
0.3098250
|
No
|
|
punctuacted_chinaopen_behave_divclose
|
Yes
|
2e-04
|
0.0005885
|
Yes
|
|
punctuacted_chinaclose_behave_divclose
|
Yes
|
1e-04
|
0.0003060
|
Yes
|
|
behave_nnopen_behave_divclose
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuated_nn_chinaopen_behave_divclose
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
behave_nnclose_behave_divclose
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuated_nn_chinaclose_behave_divclose
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuated_nn_usaopen_behave_divclose
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuated_nn_whoopen_behave_divclose
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuated_nn_usaclose_behave_divclose
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuated_nn_whoclose_behave_divclose
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuacted_usaopen_behave_divclose
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuacted_whoopen_behave_divclose
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuacted_usaclose_behave_divclose
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuacted_whoclose_behave_divclose
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
behave_convopen_behave_divopen
|
Yes
|
0.3320
|
0.3848182
|
No
|
|
behave_convclose_behave_divopen
|
Yes
|
0.2674
|
0.3247000
|
No
|
|
punctuacted_chinaopen_behave_divopen
|
Yes
|
2e-04
|
0.0005885
|
Yes
|
|
punctuacted_chinaclose_behave_divopen
|
Yes
|
1e-04
|
0.0003060
|
Yes
|
|
behave_nnopen_behave_divopen
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuated_nn_chinaopen_behave_divopen
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
behave_nnclose_behave_divopen
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuated_nn_chinaclose_behave_divopen
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuated_nn_usaopen_behave_divopen
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuated_nn_whoopen_behave_divopen
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuated_nn_usaclose_behave_divopen
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuated_nn_whoclose_behave_divopen
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuacted_usaopen_behave_divopen
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuacted_whoopen_behave_divopen
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuacted_usaclose_behave_divopen
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuacted_whoclose_behave_divopen
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
behave_convclose_behave_convopen
|
Yes
|
0.4261
|
0.4465295
|
No
|
|
punctuacted_chinaopen_behave_convopen
|
Yes
|
0.0010
|
0.0025932
|
Yes
|
|
punctuacted_chinaclose_behave_convopen
|
Yes
|
5e-04
|
0.0014167
|
Yes
|
|
behave_nnopen_behave_convopen
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuated_nn_chinaopen_behave_convopen
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
behave_nnclose_behave_convopen
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuated_nn_chinaclose_behave_convopen
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuated_nn_usaopen_behave_convopen
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuated_nn_whoopen_behave_convopen
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuated_nn_usaclose_behave_convopen
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuated_nn_whoclose_behave_convopen
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuacted_usaopen_behave_convopen
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuacted_whoopen_behave_convopen
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuacted_usaclose_behave_convopen
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuacted_whoclose_behave_convopen
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuacted_chinaopen_behave_convclose
|
Yes
|
0.0018
|
0.0044419
|
Yes
|
|
punctuacted_chinaclose_behave_convclose
|
Yes
|
9e-04
|
0.0024158
|
Yes
|
|
behave_nnopen_behave_convclose
|
Yes
|
1e-04
|
0.0003060
|
Yes
|
|
punctuated_nn_chinaopen_behave_convclose
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
behave_nnclose_behave_convclose
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuated_nn_chinaclose_behave_convclose
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuated_nn_usaopen_behave_convclose
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuated_nn_whoopen_behave_convclose
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuated_nn_usaclose_behave_convclose
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuated_nn_whoclose_behave_convclose
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuacted_usaopen_behave_convclose
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuacted_whoopen_behave_convclose
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuacted_usaclose_behave_convclose
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuacted_whoclose_behave_convclose
|
Yes
|
0.0000
|
0.0000000
|
Yes
|
|
punctuacted_chinaclose_punctuacted_chinaopen
|
Yes
|
0.4142
|
0.4370524
|
No
|
|
behave_nnopen_punctuacted_chinaopen
|
Yes
|
0.2115
|
0.2719286
|
No
|
|
punctuated_nn_chinaopen_punctuacted_chinaopen
|
Yes
|
0.1441
|
0.2060495
|
No
|
|
behave_nnclose_punctuacted_chinaopen
|
Yes
|
0.1379
|
0.2009400
|
No
|
|
punctuated_nn_chinaclose_punctuacted_chinaopen
|
Yes
|
0.0826
|
0.1330295
|
No
|
|
punctuated_nn_usaopen_punctuacted_chinaopen
|
Yes
|
0.0451
|
0.0793138
|
No
|
|
punctuated_nn_whoopen_punctuacted_chinaopen
|
Yes
|
0.0426
|
0.0775929
|
No
|
|
punctuated_nn_usaclose_punctuacted_chinaopen
|
Yes
|
0.0221
|
0.0444908
|
Yes
|
|
punctuated_nn_whoclose_punctuacted_chinaopen
|
Yes
|
0.0206
|
0.0431753
|
Yes
|
|
punctuacted_usaopen_punctuacted_chinaopen
|
Yes
|
0.0017
|
0.0042639
|
Yes
|
|
punctuacted_whoopen_punctuacted_chinaopen
|
Yes
|
0.0010
|
0.0025932
|
Yes
|
|
punctuacted_usaclose_punctuacted_chinaopen
|
Yes
|
6e-04
|
0.0016691
|
Yes
|
|
punctuacted_whoclose_punctuacted_chinaopen
|
Yes
|
3e-04
|
0.0008660
|
Yes
|
|
behave_nnopen_punctuacted_chinaclose
|
Yes
|
0.2794
|
0.3313814
|
No
|
|
punctuated_nn_chinaopen_punctuacted_chinaclose
|
Yes
|
0.1989
|
0.2578958
|
No
|
|
behave_nnclose_punctuacted_chinaclose
|
Yes
|
0.1913
|
0.2501615
|
No
|
|
punctuated_nn_chinaclose_punctuacted_chinaclose
|
Yes
|
0.1208
|
0.1777154
|
No
|
|
punctuated_nn_usaopen_punctuacted_chinaclose
|
Yes
|
0.0697
|
0.1159141
|
No
|
|
punctuated_nn_whoopen_punctuacted_chinaclose
|
Yes
|
0.0663
|
0.1114714
|
No
|
|
punctuated_nn_usaclose_punctuacted_chinaclose
|
Yes
|
0.0363
|
0.0677305
|
No
|
|
punctuated_nn_whoclose_punctuacted_chinaclose
|
Yes
|
0.0340
|
0.0642222
|
No
|
|
punctuacted_usaopen_punctuacted_chinaclose
|
Yes
|
0.0033
|
0.0078891
|
Yes
|
|
punctuacted_whoopen_punctuacted_chinaclose
|
Yes
|
0.0020
|
0.0048571
|
Yes
|
|
punctuacted_usaclose_punctuacted_chinaclose
|
Yes
|
0.0012
|
0.0030600
|
Yes
|
|
punctuacted_whoclose_punctuacted_chinaclose
|
Yes
|
7e-04
|
0.0019125
|
Yes
|
|
punctuated_nn_chinaopen_behave_nnopen
|
Yes
|
0.3971
|
0.4219188
|
No
|
|
behave_nnclose_behave_nnopen
|
Yes
|
0.3865
|
0.4135280
|
No
|
|
punctuated_nn_chinaclose_behave_nnopen
|
Yes
|
0.2789
|
0.3313814
|
No
|
|
punctuated_nn_usaopen_behave_nnopen
|
Yes
|
0.1858
|
0.2462509
|
No
|
|
punctuated_nn_whoopen_behave_nnopen
|
Yes
|
0.1789
|
0.2401026
|
No
|
|
punctuated_nn_usaclose_behave_nnopen
|
Yes
|
0.1130
|
0.1695000
|
No
|
|
punctuated_nn_whoclose_behave_nnopen
|
Yes
|
0.1074
|
0.1666170
|
No
|
|
punctuacted_usaopen_behave_nnopen
|
Yes
|
0.0165
|
0.0350625
|
Yes
|
|
punctuacted_whoopen_behave_nnopen
|
Yes
|
0.0110
|
0.0243913
|
Yes
|
|
punctuacted_usaclose_behave_nnopen
|
Yes
|
0.0073
|
0.0169227
|
Yes
|
|
punctuacted_whoclose_behave_nnopen
|
Yes
|
0.0044
|
0.0103569
|
Yes
|
|
behave_nnclose_punctuated_nn_chinaopen
|
Yes
|
0.4890
|
0.4895000
|
No
|
|
punctuated_nn_chinaclose_punctuated_nn_chinaopen
|
Yes
|
0.3724
|
0.4135280
|
No
|
|
punctuated_nn_usaopen_punctuated_nn_chinaopen
|
Yes
|
0.2635
|
0.3247000
|
No
|
|
punctuated_nn_whoopen_punctuated_nn_chinaopen
|
Yes
|
0.2550
|
0.3215508
|
No
|
|
punctuated_nn_usaclose_punctuated_nn_chinaopen
|
Yes
|
0.1711
|
0.2358405
|
No
|
|
punctuated_nn_whoclose_punctuated_nn_chinaopen
|
Yes
|
0.1636
|
0.2296404
|
No
|
|
punctuacted_usaopen_punctuated_nn_chinaopen
|
Yes
|
0.0307
|
0.0602192
|
No
|
|
punctuacted_whoopen_punctuated_nn_chinaopen
|
Yes
|
0.0212
|
0.0432480
|
Yes
|
|
punctuacted_usaclose_punctuated_nn_chinaopen
|
Yes
|
0.0146
|
0.0319114
|
Yes
|
|
punctuacted_whoclose_punctuated_nn_chinaopen
|
Yes
|
0.0092
|
0.0210090
|
Yes
|
|
punctuated_nn_chinaclose_behave_nnclose
|
Yes
|
0.3830
|
0.4135280
|
No
|
|
punctuated_nn_usaopen_behave_nnclose
|
Yes
|
0.2726
|
0.3284079
|
No
|
|
punctuated_nn_whoopen_behave_nnclose
|
Yes
|
0.2640
|
0.3247000
|
No
|
|
punctuated_nn_usaclose_behave_nnclose
|
Yes
|
0.1782
|
0.2401026
|
No
|
|
punctuated_nn_whoclose_behave_nnclose
|
Yes
|
0.1705
|
0.2358405
|
No
|
|
punctuacted_usaopen_behave_nnclose
|
Yes
|
0.0327
|
0.0625388
|
No
|
|
punctuacted_whoopen_behave_nnclose
|
Yes
|
0.0226
|
0.0449065
|
Yes
|
|
punctuacted_usaclose_behave_nnclose
|
Yes
|
0.0156
|
0.0336169
|
Yes
|
|
punctuacted_whoclose_behave_nnclose
|
Yes
|
0.0099
|
0.0222750
|
Yes
|
|
punctuated_nn_usaopen_punctuated_nn_chinaclose
|
Yes
|
0.3794
|
0.4135280
|
No
|
|
punctuated_nn_whoopen_punctuated_nn_chinaclose
|
Yes
|
0.3694
|
0.4135280
|
No
|
|
punctuated_nn_usaclose_punctuated_nn_chinaclose
|
Yes
|
0.2662
|
0.3247000
|
No
|
|
punctuated_nn_whoclose_punctuated_nn_chinaclose
|
Yes
|
0.2564
|
0.3215508
|
No
|
|
punctuacted_usaopen_punctuated_nn_chinaclose
|
Yes
|
0.0612
|
0.1052090
|
No
|
|
punctuacted_whoopen_punctuated_nn_chinaclose
|
Yes
|
0.0441
|
0.0793138
|
No
|
|
punctuacted_usaclose_punctuated_nn_chinaclose
|
Yes
|
0.0317
|
0.0613937
|
No
|
|
punctuacted_whoclose_punctuated_nn_chinaclose
|
Yes
|
0.0211
|
0.0432480
|
Yes
|
|
punctuated_nn_whoopen_punctuated_nn_usaopen
|
Yes
|
0.4895
|
0.4895000
|
No
|
|
punctuated_nn_usaclose_punctuated_nn_usaopen
|
Yes
|
0.3755
|
0.4135280
|
No
|
|
punctuated_nn_whoclose_punctuated_nn_usaopen
|
Yes
|
0.3641
|
0.4135280
|
No
|
|
punctuacted_usaopen_punctuated_nn_usaopen
|
Yes
|
0.1079
|
0.1666170
|
No
|
|
punctuacted_whoopen_punctuated_nn_usaopen
|
Yes
|
0.0811
|
0.1320032
|
No
|
|
punctuacted_usaclose_punctuated_nn_usaopen
|
Yes
|
0.0606
|
0.1052090
|
No
|
|
punctuacted_whoclose_punctuated_nn_usaopen
|
Yes
|
0.0424
|
0.0775929
|
No
|
|
punctuated_nn_usaclose_punctuated_nn_whoopen
|
Yes
|
0.3855
|
0.4135280
|
No
|
|
punctuated_nn_whoclose_punctuated_nn_whoopen
|
Yes
|
0.3741
|
0.4135280
|
No
|
|
punctuacted_usaopen_punctuated_nn_whoopen
|
Yes
|
0.1129
|
0.1695000
|
No
|
|
punctuacted_whoopen_punctuated_nn_whoopen
|
Yes
|
0.0851
|
0.1342299
|
No
|
|
punctuacted_usaclose_punctuated_nn_whoopen
|
Yes
|
0.0639
|
0.1086300
|
No
|
|
punctuacted_whoclose_punctuated_nn_whoopen
|
Yes
|
0.0448
|
0.0793138
|
No
|
|
punctuated_nn_whoclose_punctuated_nn_usaclose
|
Yes
|
0.4880
|
0.4895000
|
No
|
|
punctuacted_usaopen_punctuated_nn_usaclose
|
Yes
|
0.1787
|
0.2401026
|
No
|
|
punctuacted_whoopen_punctuated_nn_usaclose
|
Yes
|
0.1400
|
0.2020755
|
No
|
|
punctuacted_usaclose_punctuated_nn_usaclose
|
Yes
|
0.1089
|
0.1666170
|
No
|
|
punctuacted_whoclose_punctuated_nn_usaclose
|
Yes
|
0.0798
|
0.1312839
|
No
|
|
punctuacted_usaopen_punctuated_nn_whoclose
|
Yes
|
0.1867
|
0.2462509
|
No
|
|
punctuacted_whoopen_punctuated_nn_whoclose
|
Yes
|
0.1468
|
0.2079667
|
No
|
|
punctuacted_usaclose_punctuated_nn_whoclose
|
Yes
|
0.1147
|
0.1703796
|
No
|
|
punctuacted_whoclose_punctuated_nn_whoclose
|
Yes
|
0.0844
|
0.1342299
|
No
|
|
punctuacted_whoopen_punctuacted_usaopen
|
Yes
|
0.4364
|
0.4511432
|
No
|
|
punctuacted_usaclose_punctuacted_usaopen
|
Yes
|
0.3776
|
0.4135280
|
No
|
|
punctuacted_whoclose_punctuacted_usaopen
|
Yes
|
0.3135
|
0.3661489
|
No
|
|
punctuacted_usaclose_punctuacted_whoopen
|
Yes
|
0.4397
|
0.4515040
|
No
|
|
punctuacted_whoclose_punctuacted_whoopen
|
Yes
|
0.3723
|
0.4135280
|
No
|
|
punctuacted_whoclose_punctuacted_usaclose
|
Yes
|
0.4309
|
0.4484878
|
No
|
import numpy as np
import pandas as pd
import seaborn as sns
import glob
import os
import matplotlib.pyplot as plt
from matplotlib.pyplot import figure
path = "/Users/scrockford/Library/CloudStorage/OneDrive-FondazioneIstitutoItalianoTecnologia/punctuated_similarity_proof/results/FTSE/*.csv"
cmap = 'mako'
for fname in glob.glob(path):
data2plot = pd.read_csv(fname)
plotname = os.path.basename(fname)
plotname = plotname.replace(".csv", "")
plt.figure()
plt.title(plotname, fontsize = 20)
sns.heatmap(data2plot,
cmap = cmap,
square=True,
cbar=True,
xticklabels=False,
yticklabels=False)
plt.show()
import numpy as np
import pandas as pd
import seaborn as sns
import glob
import os
import matplotlib.pyplot as plt
from matplotlib.pyplot import figure
path = "/Users/scrockford/Library/CloudStorage/OneDrive-FondazioneIstitutoItalianoTecnologia/punctuated_similarity_proof/results/GSPC/*.csv"
cmap = 'mako'
for fname in glob.glob(path):
data2plot = pd.read_csv(fname)
plotname = os.path.basename(fname)
plotname = plotname.replace(".csv", "")
plt.figure()
plt.title(plotname, fontsize =20)
sns.heatmap(data2plot,
cmap = cmap,
square=True,
cbar=True,
xticklabels=False,
yticklabels=False)
plt.show()
import numpy as np
import pandas as pd
import seaborn as sns
import glob
import os
import matplotlib.pyplot as plt
from matplotlib.pyplot import figure
path = "/Users/scrockford/Library/CloudStorage/OneDrive-FondazioneIstitutoItalianoTecnologia/punctuated_similarity_proof/results/GDAXI/*.csv"
cmap = 'mako'
for fname in glob.glob(path):
data2plot = pd.read_csv(fname)
plotname = os.path.basename(fname)
plotname = plotname.replace(".csv", "")
plt.figure()
plt.title(plotname, fontsize =20)
sns.heatmap(data2plot,
cmap = cmap,
square=True,
cbar=True,
xticklabels=False,
yticklabels=False)
plt.show()
import numpy as np
import pandas as pd
import seaborn as sns
import glob
import os
import matplotlib.pyplot as plt
from matplotlib.pyplot import figure
path = "/Users/scrockford/Library/CloudStorage/OneDrive-FondazioneIstitutoItalianoTecnologia/punctuated_similarity_proof/results/FTSEMIB/*.csv"
cmap = 'mako'
for fname in glob.glob(path):
data2plot = pd.read_csv(fname)
plotname = os.path.basename(fname)
plotname = plotname.replace(".csv", "")
plt.figure()
plt.title(plotname, fontsize =20)
sns.heatmap(data2plot,
cmap = cmap,
square=True,
cbar=True,
xticklabels=False,
yticklabels=False)
plt.show()
import numpy as np
import pandas as pd
import seaborn as sns
import glob
import os
import matplotlib.pyplot as plt
from matplotlib.pyplot import figure
path = "/Users/scrockford/Library/CloudStorage/OneDrive-FondazioneIstitutoItalianoTecnologia/punctuated_similarity_proof/results/FCHI/*.csv"
cmap = 'mako'
for fname in glob.glob(path):
data2plot = pd.read_csv(fname)
plotname = os.path.basename(fname)
plotname = plotname.replace(".csv", "")
plt.figure()
plt.title(plotname, fontsize =20)
sns.heatmap(data2plot,
cmap = cmap,
square=True,
cbar=True,
xticklabels=False,
yticklabels=False)
plt.show()
import numpy as np
import pandas as pd
import seaborn as sns
import glob
import os
import matplotlib.pyplot as plt
from matplotlib.pyplot import figure
path = "/Users/scrockford/Library/CloudStorage/OneDrive-FondazioneIstitutoItalianoTecnologia/punctuated_similarity_proof/results/AXJO/*.csv"
cmap = 'mako'
for fname in glob.glob(path):
data2plot = pd.read_csv(fname)
plotname = os.path.basename(fname)
plotname = plotname.replace(".csv", "")
plt.figure()
plt.title(plotname, fontsize =20)
sns.heatmap(data2plot,
cmap = cmap,
square=True,
cbar=True,
xticklabels=False,
yticklabels=False)
plt.show()
import numpy as np
import pandas as pd
import seaborn as sns
import glob
import os
import matplotlib.pyplot as plt
from matplotlib.pyplot import figure
path = "/Users/scrockford/Library/CloudStorage/OneDrive-FondazioneIstitutoItalianoTecnologia/punctuated_similarity_proof/results/HSI/*.csv"
cmap = 'mako'
for fname in glob.glob(path):
data2plot = pd.read_csv(fname)
plotname = os.path.basename(fname)
plotname = plotname.replace(".csv", "")
plt.figure()
plt.title(plotname, fontsize =20)
sns.heatmap(data2plot,
cmap = cmap,
square=True,
cbar=True,
xticklabels=False,
yticklabels=False)
plt.show()
